Liquid crystal module character display method

Abstract

The invention relates to the technical field of liquid crystal display, and discloses a liquid crystal module character display method, which comprises the following steps of: performing stroke decomposition and topological relation establishment on Chinese characters in a target character set to generate a character stroke data set; performing two-dimensional character topological mapping and compressed encoding on the to-be-displayed characters to form compressed character description data; executing hierarchical rendering and stroke synthesis in a character reconstruction engine to generate a target character dot matrix; pixel array electric field distribution optimization is carried out, and sub-pixel-level precision character edge display data are obtained by controlling the driving voltage gradient; performing electric field compensation based on the character edge display data with sub-pixel-level precision to form character display data with enhanced edge sharpness; stroke self-adaptive anti-aliasing processing is carried out on the character display data with enhanced edge sharpness, clear Chinese character display data is output, the visual smoothness of character edges in a small-size display environment is optimized, and the overall recognition degree of characters is improved.

Description

Technical Field

[0001] This invention relates to the field of liquid crystal display technology, and in particular to a method for displaying characters on a liquid crystal module. Background Technology

[0002] With the widespread adoption of portable electronic devices, small-sized display modules, such as 1.44-inch TFT color LCD screens, are widely used in products like smartwatches, portable testing instruments, and medical devices. However, in these miniature display environments with only 128×128 pixels, clearly displaying complex Chinese characters and other characters faces significant challenges. Traditional dot-matrix storage methods consume a large amount of MCU Flash resources and are prone to problems such as blurred character edges and severe jagged edges on small displays, seriously affecting user experience and information transmission efficiency.

[0003] Current character display technology for small-sized LCD modules mainly relies on the storage and retrieval of complete dot matrix data. For embedded devices with limited memory resources, this makes it difficult to support the display needs of a large number of Chinese character sets. At the same time, due to pixel density limitations, traditional rendering methods cannot effectively utilize limited pixel resources when presenting complex characters in a small display space, resulting in character structure distortion, loss of stroke details, and decreased recognition, especially when displaying small font sizes. Summary of the Invention

[0004] This invention provides a method for displaying characters on a liquid crystal module. This invention optimizes the visual smoothness of character edges in small-size display environments and improves the overall recognizability of characters.

[0005] In a first aspect, the present invention provides a method for displaying characters on a liquid crystal module, the method comprising: Stroke decomposition and topological relationship establishment are performed on Chinese characters in the target character set to generate a character stroke dataset; Based on the character stroke dataset, perform two-dimensional character topology mapping and compression encoding on the characters to be displayed to form compressed character description data; The compressed character description data is input into the character reconstruction engine to perform hierarchical rendering and stroke synthesis to generate the target character dot matrix; The pixel array electric field distribution of the character edge portion in the target character dot matrix is ​​optimized, and the driving voltage gradient is controlled to obtain character edge display data with sub-pixel accuracy; Electric field compensation is performed on the character edge display data with subpixel precision to form character display data with enhanced edge sharpness; The character display data with enhanced edge sharpness is subjected to stroke-adaptive anti-aliasing processing to output clear Chinese character display data.

[0006] The technical solution provided by this invention decomposes complex characters such as Chinese characters into basic stroke units and establishes topological relationships through two-dimensional character topology mapping and compression encoding technology. Compared with traditional dot matrix storage methods, this significantly reduces storage requirements and increases the number of characters supported under the same hardware conditions. The character reconstruction engine adopts a hierarchical rendering and stroke synthesis strategy, combined with a dual-layer cache structure and on-demand rendering mechanism, reducing CPU load, decreasing rendering latency for character display, and improving system response speed. Through pixel array electric field distribution optimization technology, the driving voltage gradient is precisely controlled to achieve sub-pixel-level precision character edge display, enabling the 1.44-inch TFT color LCD screen to achieve a visual effect similar to high-resolution display. The electric field compensation algorithm effectively solves the problem of electric field interference between adjacent pixels, suppresses edge blurring, and makes the character edge contours sharper, especially when displaying small font sizes. The stroke adaptive anti-aliasing processing technology optimizes the visual smoothness of character edges in small-size display environments through edge detection, direction-dependent filtering, and transparency mixing, improving the overall recognizability of characters. Attached Figure Description

[0007] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 This is a schematic diagram illustrating the steps of the character display method of the liquid crystal module in an embodiment of the present invention. Detailed Implementation

[0009] This invention provides a method for displaying characters on a liquid crystal module. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0010] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1One embodiment of the character display method for a liquid crystal module in this invention includes: Step S1: Perform stroke decomposition and topological relationship establishment on the Chinese characters in the target character set to generate a character stroke dataset; It is understood that the executing entity of this invention can be a liquid crystal module character display device, a terminal, or a server; no specific limitation is made here. This embodiment of the invention will be described using a server as an example.

[0011] Specifically, for all Chinese character samples in the target character set, semantic-level structural classification is performed based on stroke structure. All characters are deconstructed into composite units composed of basic components such as horizontal, vertical, left-falling, right-falling, and dot strokes, and these are categorized by stroke type to form basic stroke classification results. Each basic stroke extracted from the basic stroke classification results is mapped to a fixed pixel coordinate space of 128×128 on the LCD screen, and each stroke is depicted point by point. Geometric constraints are established under the pixel grid to reconstruct the outline structure of Chinese character strokes on the 1.44-inch TFT color LCD screen with limited display resolution. This process considers the starting point, ending point, and control point positions of each stroke and constructs a dot matrix template to form the initial geometric foundation for algorithm processing. The Bezier curve algorithm is used to approximate the continuous curve structure existing in traditional strokes within the stroke dot matrix template. By selecting an appropriate order of Bezier curve and adjusting its control point positions, a smooth restoration of stroke curvature changes is achieved, establishing an optimal balance between curvature description accuracy and computational resource consumption, resulting in an optimized stroke template with high descriptive capabilities. The optimized stroke templates undergo pixel-level control, identifying and filtering the smallest identifiable stroke regions within a unit pixel grid to ensure accurate representation of the basic stroke shape even within extremely small areas of 3×3 or 5×5 pixels. After precision verification and shape constraints, each identifiable stroke unit is assigned a unique identifier (ID), achieving independent stroke management both logically and physically, forming a basic stroke set with a unique index attribute. The storage format of this basic stroke set is optimized, employing a fixed-length structure to organize vector path data and pixel mapping data. Redundant coordinate information is compressed, and a symmetric reuse strategy is introduced, resulting in each stroke occupying only approximately 16 bytes of Flash storage space on average. After format compression and content integration, the final character stroke dataset contains the topological description, geometric matrix, pixel mapping, and unique index identifier for all basic strokes.

[0012] Step S2: Based on the character stroke dataset, perform two-dimensional character topology mapping and compression encoding on the characters to be displayed to form compressed character description data; Specifically, all basic stroke types related to the target character are extracted from the character stroke dataset. Structural analysis is then used to identify the stroke arrangement order, configuration position, and spatial combination method, forming character structure data that includes individual stroke attributes and global layout logic. This structure data indicates the layout relationship of each stroke within the character and marks the relative positional features (such as top-bottom, left-right, inside-outside, overlapping) and connection relationships (such as stroke junctions, intersections, nested structures, etc.) between strokes. This allows for the construction of a character topology graph, where the edge set describes the spatial semantics of the strokes, and the vertex set describes the stroke instances themselves, thus generating topology data with semantic deconstructibility. An encoding format is designed for the topology data. Fixed-length binary encoding is used to identify 16 basic spatial relationships between strokes, with each relationship edge represented by a 4-bit code. An adjacency matrix is ​​used to structurally represent the entire stroke topology graph, ensuring that each cell in the matrix represents the spatial connection type between any two strokes. After completing the structural representation, an improved Burrows-Wheeler transform (BWT) algorithm is used to process the topology encoding matrix. This transformation expands the adjacency matrix into a one-dimensional feature sequence, generates a transformation matrix through cyclic shifting and lexicographical sorting, and extracts the last column to form the compressed topological coding sequence. Since BWT has the advantage of improving the clustering of local repetitive patterns, this processing result has a higher data entropy compression ratio in subsequent character set compression. A fine-tuning parameter set is introduced into the compressed topological coding sequence to supplement the detailed positional information of the strokes. The fine-tuning parameter set accompanying each stroke includes horizontal and vertical offset values ​​δx and δy, with values ​​ranging from [-3,3] in pixels, used to describe the fine offset of the stroke from its standard position. The fine-tuning data is combined with the compressed topological coding sequence, enabling each logical structure to have the ability to map in a true two-dimensional pixel grid, forming coded data with precise positioning information. The stroke ID sequence of the character to be displayed is combined with the coded data with precise positioning information to obtain compressed character description data.

[0013] Step S3: Input the compressed character description data into the character reconstruction engine to perform hierarchical rendering and stroke synthesis to generate the target character dot matrix; Specifically, a character reconstruction engine system is constructed. This engine features a pre-defined dual-layer cache structure for managing basic stroke data and complete character dot matrices, respectively. 2KB of RAM is dedicated to the stroke cache for high-speed reading and reuse of frequently used basic stroke dot matrices, while 14KB of RAM is used for the character cache, maximizing character dot matrice retention to support parallel reconstruction and multi-character display switching. A least recently used replacement strategy is employed for dynamic resource scheduling, ensuring stable and efficient data caching capabilities even in memory-constrained microcontroller environments. Based on this, compressed character description data is input into the character reconstruction engine, and the decoding module is activated to parse its content. The stroke ID sequence is extracted to identify which basic strokes constitute the character. Simultaneously, the topological relationship encoding and fine-tuning parameter set are parsed to reconstruct the character's structural diagram and stroke connection relationships, thereby forming character structural description information. This description information is used to reconstruct the structural position and combination of each stroke within the character. Based on the structural description information, the precise placement position of each stroke in the character display area is calculated, taking into account the basic topological position, fine-tuning displacement parameters, and the overall character center of gravity balance. Stroke positioning data is output to guide the calculation of the actual pixel rendering position. A stroke deformation algorithm is then executed on the stroke positioning data. This algorithm, based on the relative geometric position of the strokes in the character structure, uses a parameter set including scaling factors (sx, sy), rotation angle θ, and curvature adjustment coefficients k1 and k2 to transform the original stroke template, generating a deformed stroke dot matrix that conforms to the current character shape requirements. Subsequently, according to the connection logic defined by the topological relationship diagram, the deformed strokes are combined in the correct order and spatial proportions to form a structurally complete character dot matrix. A dual-layer caching structure is employed to manage the synthesized complete character dot matrix: if the current character already exists in the character cache, the image data is directly retrieved from the cache for display, avoiding redundant calculations; if it is a new character, it is determined whether the cache is full, and the cache is updated according to the LRU strategy. At the same time, the required stroke dot matrix from the stroke cache is loaded into the cache, improving the efficiency of subsequent reconstruction of similar characters. The final result is a target character dot matrix directly usable on a 1.44-inch TFT color LCD screen.

[0014] Step S4: Optimize the pixel array electric field distribution of the character edge portion in the target character dot matrix and obtain character edge display data with sub-pixel accuracy by controlling the driving voltage gradient; Specifically, key physical characteristics such as pixel pitch, liquid crystal molecule type, alignment layer processing method, and backlight characteristics are extracted from the manufacturing parameters of a 1.44-inch TFT display screen. Pixel pitch determines the smallest resolvable unit between pixel centers; liquid crystal molecule type (e.g., TN or VA) determines the deflection characteristics of molecules in response to an electric field; the alignment layer processing method determines the directionality of the initial alignment state; and the color temperature and brightness uniformity of the backlight affect the final visual presentation. These parameters collectively constitute a set of physical characteristic parameters for describing the response characteristics of the liquid crystal screen. Based on these physical characteristic parameters, a nonlinear mapping function F(V)=θ between the pixel driving voltage V and the liquid crystal molecule deflection angle θ is established. This function is fitted based on experimental data and expressed as a piecewise polynomial to accurately reflect the response effect of different voltage levels on the liquid crystal molecules. Simultaneously, for pixels located at the edges of the target character dot matrix, edge pixel distance analysis is performed to calculate the normalized distance from each edge pixel to the ideal boundary of the character. This distance represents the positional gradient of the pixel within the character structure. This process is achieved through contour scanning and distance mapping algorithms. The outer contour of the character is discretized into a pixel-level distribution, and the relative depth of each edge point is obtained accordingly. Substituting the edge distances into the mapping function, the edge distances are converted into grayscale voltage control weights, thereby solving for the optimal driving voltage corresponding to each edge pixel. This driving voltage causes the liquid crystal molecules controlled by the edge pixels to exhibit a deflection state close to sub-pixel transition, achieving refined grayscale control and making the character edges appear as a continuous and smooth grayscale transition rather than an abrupt boundary. Based on the optimal driving voltage of each edge pixel, combined with the row and column control architecture of the LCD screen, and based on the driving electric field generation mechanism at the intersection of the row scanning electrodes and column data electrodes, the instantaneous distribution characteristics of the electric field application area are calculated, thereby generating electric field distribution control parameters. Based on the electric field distribution control parameters, a driving voltage matrix is ​​constructed, where each element represents the voltage driving value at the corresponding position on the screen. Through an optimized row and column scanning strategy, the matrix is ​​transmitted row by row and column by column to the source and gate channels of the display controller, forming a synchronously transmitted voltage waveform, thereby driving the liquid crystal molecules to form the desired alignment angle, achieving a character edge performance effect with sub-pixel resolution.

[0015] The optimal driving voltage for each edge pixel is decomposed into a time sequence, transforming the static voltage value into a dynamic driving signal with a controllable waveform, forming multi-stage driving timing waveform data. This waveform includes a pre-charging stage, a main driving stage, and a holding stage. Based on the multi-stage waveform data, software-level instruction reconstruction and scheduling strategy optimization are performed on the standard 128×128 TFT driving circuit. That is, without changing the existing hardware framework, a fine-grained software control module is introduced to expand the control channels of the TFT-Gate driving and Source driving, enabling timing adjustments to the row scan pulses and column data levels. This generates a driving timing control data structure where the control behavior of each row-column intersection is precisely specified on the time axis, supporting fine-tuning of charge migration paths, electrode charging and discharging rates, and holding waveform shapes. Based on this, the potential response of each electrode intersection is dynamically modeled according to the driving timing control data, and the actual electric field strength and direction distribution acting on the liquid crystal pixel unit are calculated. By applying the driving waveform to the liquid crystal molecules, their deflection angle changes at the nanoscale are induced, thereby forming sub-pixel-level molecular arrangement data. These molecular arrangement information reflect the spatial details of the optical delay and transmittance response in the liquid crystal layer, and are a direct determinant of the final image sharpness. To improve the coherence of edge transitions and the continuity of electric field effects, the spatial electric field coupling relationship between the electric field output of each pixel and its neighboring pixels is incorporated into the modeling analysis. That is, diffusion modeling and interaction analysis are performed on the electric field at the edge pixels to identify the potential shift and molecular arrangement error caused by electric field interference from neighboring pixels, and electric field interaction compensation data is generated based on this. The original optimal voltage value of each edge pixel is combined with the above compensation data to perform voltage gradient processing, expanding the pixel voltage from single-value control to a spatial gradient control form, so that the pixel voltage in the edge region presents a continuous and differentiable potential distribution in space, avoiding abrupt changes in the electric field boundary, thereby improving the overall alignment consistency of liquid crystal molecules and generating an optimized voltage gradient distribution map. This map represents the target voltage value that all pixels on the screen should receive within a specific refresh cycle using a two-dimensional tensor structure. The optimized voltage gradient distribution map is converted into a control instruction sequence supported by the LCD driver chip (such as ST7735S). This instruction set is reorganized according to the row scanning order and column data transmission logic inside the LCD screen to meet hardware constraints such as bus timing, level flipping, and data packet composition. It is then sent to the driver controller at high speed through SPI or parallel interface and accurately loaded onto the row and column control electrodes within the frame refresh cycle, thereby outputting electric field distribution control parameters with sub-pixel edge accuracy.

[0016] Step S5: Perform electric field compensation on the character edge display data with sub-pixel precision to form character display data with enhanced edge sharpness; Specifically, based on the physical structure parameters and pixel arrangement of the LCD screen, the electric field interference between any central pixel and its eight surrounding adjacent pixels is quantitatively measured. An electric field influence coefficient matrix A[3×3] is constructed through experimental measurement or field simulation fitting. Each element α(i,j) in the matrix represents the degree of superposition interference of the corresponding neighboring pixels on the electric field intensity of the central pixel. The coupling coefficient decreases with distance, exhibiting symmetry and reflecting the difference between the screen edge effect and the central region. The target display electric field distribution is calculated inversely based on the coupling coefficient matrix. That is, a constraint relationship is established using the superposition relationship of electric fields between pixels, and the initial driving voltage distribution required to generate the target electric field is derived. This process adopts a reverse electric field compensation strategy, starting from the desired electric field and using the coupling matrix to solve for the linear combination inverse solution of the ideal driving voltage, forming the initial solution of the compensated driving voltage. Because the response of liquid crystal molecules is nonlinear and sensitive to local optima, an iterative optimization algorithm is executed based on the initial solution to improve the solution accuracy. The current driving voltage is input into the liquid crystal electric field mapping model to obtain the error between the actual electric field and the target electric field. Then, the voltage distribution is corrected in a gradient manner to form an update rule. Through multiple iterations, the error convergence range is gradually approached to obtain the final compensated driving voltage value. Considering that the 1.44-inch TFT LCD screen operates in an environment between -10℃ and 50℃, and that the liquid crystal material is sensitive to temperature changes, with its viscosity, response speed, and driving voltage threshold all changing with temperature, a temperature adaptive adjustment mechanism is introduced into the final compensated driving voltage value. This mechanism introduces a temperature-sensitive factor to adjust the voltage bias amplitude and response gradient based on the current ambient temperature, ensuring consistent display performance across the entire temperature range. After temperature compensation, to ensure efficiency and real-time performance in the embedded display controller, linear interpolation optimization is performed on the temperature-compensated driving voltage data. The voltage values ​​of discrete sampling points are expanded into a fitted voltage surface in the continuous pixel space, giving the generated electric field compensation data spatial continuity and smooth transition. This method identifies the positions of all character edge pixels from sub-pixel precision character edge display data. These pixels satisfy the boundary condition that they have at least one neighboring pixel that is not a character pixel. For these edge pixels, an optimized compensation voltage is applied to the driving data at the corresponding position, forming a voltage matrix with sub-pixel grayscale precision and corrected by reverse electric field coupling. This achieves an ideal alignment of liquid crystal molecules in the edge region. The resulting character display data exhibits higher structural sharpness and grayscale distribution uniformity at the edges, improving the character display's outline clarity, anti-aliasing capability, and visual continuity of edge transitions.

[0017] Step S6: Perform stroke-adaptive anti-aliasing on the character display data with enhanced edge sharpness to output clear Chinese character display data.

[0018] Specifically, edge detection is performed on the character display data with enhanced edge sharpness. By analyzing the changing trends of pixel grayscale gradients in the character dot matrix, all pixels with significant brightness jumps are identified, forming a set of character edge pixels. These pixels are located at stroke boundaries and determine the smoothness of the character outline. For each edge pixel, its local direction vector is calculated. This vector represents the tangent direction or dominant gradient direction of the edge outline at the current pixel, serving as edge direction feature data to provide a basis for subsequent directional processing. A direction-dependent filtering matrix is ​​constructed based on the edge direction feature data, adaptively adjusting the filtering weights to ensure high preservation in the edge direction and strong smoothness in the normal direction. Specifically, this filtering matrix is ​​a 3×3 or 5×5 local kernel matrix, whose element values ​​vary with the direction vector. For example, a larger weight is distributed in the edge direction to avoid edge blurring, while the pixel fusion effect is enhanced in the vertical direction, thereby achieving a dynamic balance between jagged edge suppression and boundary detail preservation. Simultaneously, for each pixel in the character edge pixel set, its normalized distance to the ideal character boundary is calculated. This distance measures the depth of the pixel's position in the edge transition zone and is obtained through contour distance transformation or comparison of inner and outer contours. Based on this distance, a smooth transition function is applied to map it to a transparency value, resulting in edge transparency data. This transparency describes the degree of participation of each pixel in visual blending, i.e., the proportion at which it should be blended in grayscale or color between the foreground and background. The character rendering process based on edge transparency data is divided into two stages: geometric correction and color blending. In the geometric correction stage, the position of edge pixels is fine-tuned at sub-pixel precision. The position of the stroke boundary pixels is calibrated by a small range of coordinate offsets, so that the stroke curve edge and the pixel grid are geometrically aligned, improving contour smoothness. In the color blending stage, based on the calculated transparency, the foreground color (i.e., the character color) and the background color (usually the background interface color) are linearly blended to achieve a natural and smooth display of the color transition zone, avoiding jagged edges or abrupt changes at the edges. To adapt to the display requirements of different character sizes, a parameter adjustment mechanism based on font size is introduced to dynamically adjust parameters such as filter matrix weights, transparency mapping function slopes, and geometric fine-tuning amplitudes involved in the anti-aliasing process. For small-sized characters, a conservative strategy is adopted to prevent information blurring and detail loss, reducing the filter kernel size and slowing down transparency changes. For large-sized characters, more aggressive interpolation and filtering settings are used to enhance edge smoothness. Through all the above processing steps, a set of pixel datasets that have undergone orientation-aware filtering, transparency blending, and geometric correction is output.

[0019] In this embodiment of the invention, complex characters such as Chinese characters are decomposed into basic stroke units and topological relationships are established through two-dimensional character topology mapping and compression encoding technology. Compared with traditional dot matrix storage methods, this significantly reduces storage requirements and increases the number of characters supported under the same hardware conditions. The character reconstruction engine adopts a hierarchical rendering and stroke synthesis strategy, combined with a dual-layer cache structure and on-demand rendering mechanism, reducing CPU load, reducing rendering latency for character display, and improving system response speed. Through pixel array electric field distribution optimization technology, the driving voltage gradient is precisely controlled to achieve sub-pixel-level precision character edge display, enabling the 1.44-inch TFT color LCD screen to achieve a visual effect similar to high-resolution display. The electric field compensation algorithm effectively solves the problem of electric field interference between adjacent pixels, suppresses edge blurring, and makes the character edge contours sharper, especially when displaying small font sizes. The stroke adaptive anti-aliasing processing technology optimizes the visual smoothness of character edges in small-size display environments through edge detection, direction-dependent filtering, and transparency mixing, improving the overall recognizability of characters.

[0020] In one specific embodiment, the process of performing step S1 may specifically include the following steps: The Chinese characters in the target character set are classified according to their stroke structure to obtain the basic stroke classification results; For each basic stroke in the basic stroke classification results, plot points in a 128×128 pixel coordinate system to obtain a stroke dot matrix template. The Bezier curve algorithm is used to process the continuous curves of traditional strokes in the stroke dot matrix template to obtain an optimized stroke template. The optimized stroke template is controlled at the pixel level to obtain recognizable stroke units in a micro display environment, and each stroke in the recognizable stroke unit is assigned a unique identifier ID to obtain a set of basic strokes with identifiers. The storage format of the labeled basic stroke set is optimized, and the vector description data and pixel mapping data of all basic strokes are integrated to output a character stroke dataset.

[0021] Specifically, the Chinese characters in the target character set are deconstructed into stroke elements that constitute their basic structure, and standardized and classified according to the writing method, geometric structure, and direction of movement of the strokes. This classification work is transformed into a classification modeling process of graphic primitives in the computer graphics system, including the morphological merging of basic components such as horizontal, vertical, left-falling, right-falling, rising, turning, hook, and dot. To cover the complexity of traditional Chinese character strokes, the classification is refined to at least thirty-eight basic stroke forms, and an scalable stroke structure system is established by combining multi-source character shape sample data to form the basic stroke classification results. Using the actual pixel distribution of the LCD display module as a reference frame, each type of basic stroke is spatially mapped in a 128×128 pixel coordinate system. This stage involves describing the geometric position of the strokes in the two-dimensional plane and determining control parameters such as their starting point, ending point, intermediate control points, and width variation process. The point plotting method combines raster scanning and contour vector extraction, discretizing the abstract stroke structure into a dot matrix trajectory that can be rendered in the screen pixel raster and recorded in the form of vector chains. The creation of dot matrix templates must fully consider the directional characteristics and deformation constraints of strokes, ensuring that the template retains the geometric features of the original strokes after rotation, scaling, or symmetry transformations. Each stroke template includes its original point set, connection order, control weights, and geometric characteristic markers, ensuring that subsequent stages perform parametric transformations without distortion. Considering that many strokes in traditional Chinese characters have significant curved features, approximating them with broken line segments would lead to numerous jagged edges and geometric errors. Therefore, the Bezier curve algorithm is introduced to fit and optimize the continuous curve structure in the template. The Bezier curve, as a parametric polynomial interpolation function, describes complex curve trajectories with very few control points. During the construction process, a second-order or third-order Bezier curve model is selected based on the geometric structure of each stroke. The starting point, ending point, and intermediate bending feature points of the stroke are used as the control point set. The algorithm iteratively calculates the Bezier curve segment with the optimal approximation accuracy, thereby expressing the continuous structure of the stroke in the form of a smooth function, ensuring consistency and high fidelity during scaling and deformation processing, resulting in an optimized stroke template. The optimized stroke template is subjected to pixel-level control, mapping it back to discrete point matrix coordinates in a 128×128 pixel space, and then further analyzed and controlled at the pixel level. The core objective of pixel-level control is to express the key morphological features of the stroke curve within the smallest possible pixel area, while avoiding structural blurring or information loss in micro-display environments. Using 3×3 to 5×5 pixels as the smallest recognizable unit, and combining curvature analysis, edge-preserving filtering, and region density constraint strategies, spatial sampling, anti-aliasing approximation, and local symmetry optimization are performed on the Bezier curve to generate recognizable stroke units suitable for micro-LCD modules.Each stroke in the identifiable stroke unit is assigned a unique identifier ID, generated based on stroke type, geometric parameters, and path encoding. This ensures rapid indexing and interface with the character topology mapping system during character reconstruction. All identified stroke units together constitute an identifiable basic stroke set. Each unit in the set contains its graphic data, unique ID, control parameter structure, deformable constraints, and resolution adaptation level. The storage format of the identifiable basic stroke set is optimized. A fixed-length field combination method is used to compactly encapsulate vector data, pixel arrays, and control parameters, avoiding redundant storage overhead caused by dynamic data structures. Simultaneously, differential encoding and symbol mapping compression methods are introduced for control point sequences and path descriptions to improve overall data entropy density. Template reuse is achieved by incorporating spatial symmetry, allowing the use of mirrored or rotated versions of the same stroke template in different transformation scenarios. After compression and optimization, the average storage size of each basic stroke in the stroke dataset is compressed to less than 16 bytes. It is loaded and run in the system as a static lookup table, and with a fast decoding mechanism, millisecond-level rendering response is achieved. The final output is a character stroke dataset.

[0022] In one specific embodiment, the process of performing step S2 may specifically include the following steps: Based on the character stroke dataset, structural analysis is performed on the characters to be displayed to obtain character structure data. Then, based on the character structure data, the relative position information and connection information between strokes are recorded to obtain topological relationship data. A standardized topological relationship code is obtained by designing an encoding format for the topological relationship data. The standardized topological relation code is processed by the Burrows-Wheeler transform to obtain the compressed topological code sequence. A fine-tuning parameter set is introduced into the compressed topological coding sequence to accurately locate the position of each stroke within the character, resulting in encoded data with precise positioning information. The stroke ID sequence of the character to be displayed is combined with the encoded data containing precise positioning information to form compressed character description data.

[0023] Specifically, structural analysis is performed on the characters to be displayed based on the character stroke dataset. This involves identifying the basic strokes that make up the character, as well as the positional relationships and connections of these strokes within the overall geometric framework of the character. By analyzing the stroke composition, the character is abstracted into a topological graph model composed of several stroke nodes and structural edges. Each stroke is considered a node, and the edges between nodes represent their relative positional relationships in two-dimensional space, such as top / bottom, left / right, enclosing, intersecting, or embedded. To form a structured representation, the character graphic undergoes stroke-by-stroke structural extraction, and its relative positional relationship with other strokes is calculated based on its spatial position in the standard display coordinate system. This relationship includes comparing the relative coordinates of the geometric centers and comprehensively considering the stroke coverage, intersecting areas, and overlapping control points, thereby semantically accurately classifying the connection methods of the strokes. After classifying and encoding all these relationships, topological relationship data between strokes is formed and stored as an adjacency matrix. Each element M[i][j] in the adjacency matrix represents the structural relationship category between stroke i and stroke j. This relationship is normalized to a finite set of encoded values, controlled within 16 categories, and represented by 4-bit binary numbers. An encoding format is designed for the topological relationship data. Each element in the adjacency matrix is ​​expanded into a linear sequence in row-major or column-major order, and the connection relationship between each stroke is encoded into a fixed-length binary symbol string. To compress the redundancy of the topological relationship, a sorting compression algorithm is introduced, using the Burrows-Wheeler transform to process the standard topological encoding sequence. The Burrows-Wheeler transform is a reordering algorithm that generates several permutations from the original string through cyclic shifts, then sorts all permutations lexicographically, and extracts the last character of each permutation to form a new string. In this embodiment, the topological encoding sequence is considered as the input sequence S. The Burrows-Wheeler transform maps S to a structurally highly clustered reordered sequence C, resulting in the compressed topological encoding sequence. A fine-tuning parameter set is introduced into the compressed topological encoding sequence, representing the pixel-level offset between the actual placement position of the strokes and the standard structural template. This parameter set includes the displacements δx and δy of each stroke in the x and y directions, respectively. These parameters are limited to the range [-3, 3] to meet the display requirements of small-sized LCD modules (e.g., 128×128 pixels), and their precise positioning is expressed using 6-bit encoding (3 bits per direction). For each stroke, its number, the position index of its topological connection in the compressed sequence, and the stroke's displacement fine-tuning parameters jointly constitute the stroke's spatial reconstruction metadata. This allows characters to be flexibly reconstructed in any complex form through a unified stroke call and positioning correction. Combining the fine-tuning information with the compressed topological encoding sequence forms topological description data with positioning information. This data carries both structural and positional information, enabling a complete reconstruction of the character's stroke arrangement.The topological structure data is combined with the stroke ID sequence used by the character. The ID numbers of the strokes required for each character in the stroke dataset are recorded in order and associated with the corresponding topological structure data through a structure mapping table to form a unified compressed character description data.

[0024] In one specific embodiment, the process of performing step S3 may specifically include the following steps: A dual-level cache structure is designed in the character reconstruction engine, allocating 2KB RAM space for the stroke cache area and 14KB RAM space for the character cache area. Stroke ID sequence extraction and topological relationship encoding are performed on compressed character description data to obtain complete character structure description information; The precise placement of each stroke is calculated based on the complete description information of the character structure, thus obtaining stroke positioning data; A stroke deformation algorithm is performed on the stroke positioning data to obtain a deformed stroke dot matrix. The deformed stroke dot matrix is ​​then combined according to topological relationships to obtain a synthesized complete character dot matrix. A two-level buffer structure is used to manage the buffering of the synthesized complete character dot matrix, and the target character dot matrix is ​​output.

[0025] Specifically, a two-layer cache structure is designed in the character reconstruction engine. The first-level cache, the stroke cache, is used to store and quickly reuse commonly used basic stroke units. Considering the limited number of basic strokes and the high reusability during reconstruction, 2KB of RAM space is allocated to it to meet the high-frequency retrieval requirements. The second-level cache, the character cache, is used to retain the complete character dot matrix information that has been reconstructed. It is dynamically managed using the Least Recently Used (LRU) algorithm, and 14KB of RAM space is reserved to accommodate the dot matrix data of approximately 128 characters, enabling it to handle multiple complex characters present in the interface simultaneously. After the caching mechanism is set up, the process of parsing and reconstructing the input compressed character description data is executed. This description data contains the stroke ID sequence and topological relationship encoding, supplemented by stroke position fine-tuning parameters and compressed structural index information. Therefore, the stroke ID sequence is extracted, and the stroke templates required for character construction are restored one by one. At the same time, the topological relationship encoding is decoded to recover the spatial relationships such as the connection structure between strokes within the character, the vertical and horizontal arrangement logic, and the surrounding nesting structure, thus obtaining character description information with complete structural meaning. Based on the aforementioned structural information, stroke positioning calculations are performed. This involves combining the standard position of each stroke, its structural relationship with other strokes, the set of fine-tuning parameters, and the character center alignment rules to determine the precise position of each stroke within the final display area. This position is expressed with pixel-level precision, ensuring the overall character structure is proportionally harmonious while maximizing the reproduction of each stroke's details on a micro-display (such as a 1.44-inch TFT). After positioning, based on the target position of the current stroke and character structural constraints, a series of continuous geometric transformations are performed on the standard stroke template. These include horizontal and vertical scaling transformations (parameters sx and sy), rotation operations (angle θ), and, where necessary, nonlinear curvature transformations (parameters k1 and k2) to adapt to curved structures or dynamic adjustments, generating deformed stroke dot matrix data. The deformed stroke dot matrix is ​​then combined according to topological relationships, i.e., the dot matrix data of each stroke is progressively superimposed within the character display area. Based on connection point alignment, cross-over area processing, and edge reconstruction rules, grayscale weighting, pixel fusion, and priority determination are applied to overlapping areas to form a composite character dot matrix. A two-layer caching structure is employed to manage the caching of the synthesized complete character dot matrix. First, it checks if the current character already exists in the cache. If it does, the stored procedure is skipped, and the cached result is returned directly. If it does not exist, new data is inserted based on the current cache occupancy and caching strategy, and the lowest-priority old character data is evicted if necessary. During data storage, to reduce RAM writes and power consumption, shared areas between similar characters are differentially encoded, preserving the common areas while updating only the differing parts, further reducing cache overhead.Meanwhile, to improve rendering response speed, the ID of each stroke template used by the current character and its deformation cache result are recorded in the stroke buffer so that they can be directly called when the same stroke is reused by other characters. Output the target character dot matrix.

[0026] In one specific embodiment, the process of performing step S4 may specifically include the following steps: The pixel pitch, liquid crystal molecule type, alignment layer processing method and backlight characteristics of a 1.44-inch TFT LCD screen were extracted to obtain the physical characteristic parameters of the display screen. A nonlinear mapping function between pixel driving voltage and liquid crystal molecule arrangement angle is established based on the physical characteristic parameters of the display screen. At the same time, edge pixel distance data is calculated for the character edge pixels in the target character dot matrix. The optimal driving voltage for each edge pixel is calculated based on the edge pixel distance data and the nonlinear mapping function. Based on the optimal driving voltage of each edge pixel, a precisely controlled electric field distribution is generated at the intersection of the row scanning electrode and the column data electrode, and the electric field distribution control parameters are obtained. The driving voltage matrix is ​​generated based on the electric field distribution control parameters, and the driving voltage value is transmitted to the display driving circuit through the optimized row and column scanning timing, outputting character edge display data with sub-pixel precision.

[0027] Specifically, from a hardware structure perspective, parameters are extracted and photoelectric models are constructed for the 1.44-inch TFT LCD screen to ensure that the display system can achieve high-fidelity display of tiny character edges based on physical precision control. In this process, pixel pitch data of the LCD screen is extracted, as this parameter determines the spatial potential distribution accuracy between each pixel control point; the type of liquid crystal molecules used in the LCD panel is identified, such as TN (twisted nematic), VA (vertical alignment), or IPS (planar conversion), etc. Different types of liquid crystal molecules have significantly different deflection directions, voltage response curves, and viewing angle characteristics, affecting the morphology of the electro-optical response curve; simultaneously, the alignment layer processing method is obtained, such as whether triboelectric alignment, vertical alignment, photoalignment, or polyimide coating is used, as these factors determine the initial pretilt angle and alignment stability of the liquid crystal molecules; the spectral distribution, brightness uniformity, and color temperature characteristics of the backlight are extracted to establish a mapping relationship between subsequent driving voltage control and optical output brightness. All the above factors are integrated to form a set of physical characteristic parameters for the display screen. Based on the extracted physical parameters, a nonlinear mapping function F(V)=θ between the pixel driving voltage and the alignment angle of liquid crystal molecules is constructed, where V represents the actual driving voltage applied to the pixel electrode, and θ represents the deflection angle between the liquid crystal molecules and the initial alignment direction. This function is obtained by fitting experimental data or referring to the electro-optic response curves in liquid crystal material handbooks. The function exhibits an S-shaped nonlinear variation curve, with the threshold voltage V... t The following is almost unresponsive, in the range of Vt With saturation voltage V s The growth rate is rapid, exceeding V. s The transmittance then tends to saturate and remain constant. This function determines the change in pixel transmittance at a specific voltage. Analyzing all pixels in the edge region of the target character dot matrix, the set of edge pixels is extracted, and the normalized distance from each edge pixel to the ideal character boundary is calculated. This distance is achieved through the normal distance transformation of the edge contour, with a value range of [0,1], representing the transition depth of the pixel relative to the edge core. The smaller the value, the closer the pixel is to the boundary curve; the larger the value, the more it is located inside the transition region. Using this normalized distance and the previously established nonlinear function, a reverse mapping process is designed, that is, calculating the optimal driving voltage to be applied based on the target value of the molecular deflection angle required by the pixel. Through the above calculation, each edge pixel is assigned an independent optimal driving voltage, thereby achieving sub-pixel level brightness adjustment and smooth edge transition in the liquid crystal response layer. To actually apply the above pixel-level optimal voltage value to the TFT display control circuit, the voltage distribution data is converted based on the TFT working principle. The TFT LCD screen adopts a row-column cross-control mechanism, applying voltage to each pixel through the intersection of the row scanning electrode and the column data electrode. Therefore, the driving voltage of each edge pixel is decomposed at the electrode level, mapping the pixel position (x,y) to the row electrode Ry and the column electrode Cx, generating a precisely controlled electric field distribution at this intersection. This distribution consists of four parts: electrode pre-charging, row gating, data loading, and holding voltage. Therefore, a precise driving timing waveform and voltage amplitude value are generated according to the driver IC control timing specification, thereby producing the desired electric field intensity E(x,y,t) at the spatial position (x,y) at time t. This electric field determines the instantaneous alignment direction of the liquid crystal molecules. Based on the above electric field control information, the voltage value of each edge pixel is organized into a two-dimensional matrix, where each element is the grayscale voltage to be applied to the corresponding pixel position. Simultaneously, a row and column scanning control sequence is generated based on this matrix. To maximize the stability and response speed of edge grayscale, the row and column scanning sequence, row driving width, and data holding time are optimized. For example, a longer data loading cycle or a more refined voltage step control strategy is set in the edge region to reduce grayscale drift caused by capacitance residual effects. The voltage matrix and its corresponding timing control sequence are transmitted to the display driver circuit via SPI or parallel data bus and precisely applied to the TFT row and column electrodes, achieving synchronization of the electric field precision control for each edge pixel in the display panel. After the driving process is completed, the liquid crystal molecules form a continuously controllable sub-pixel grayscale response effect in the edge pixel area, thereby making the character edges present a natural transition grayscale band, avoiding "stepped" or "serrated" edges, and improving the boundary clarity and recognition accuracy of low-sized Chinese characters on small screens.

[0028] In one specific embodiment, the process of generating a precisely controlled electric field distribution at the intersection of the row scan electrode and the column data electrode based on the optimal driving voltage of each edge pixel, and obtaining the electric field distribution control parameters, can specifically include the following steps: The optimal driving voltage for each edge pixel is decomposed into timing data to obtain multi-stage driving timing waveform data. Based on the multi-stage driving timing waveform data, the standard 128×128 driving circuit is extended in software to construct a fine control mechanism for TFT-Gate driving and Source driving, and driving timing control data is obtained. Based on the driving timing control data, the potential distribution at the intersection of each row and column electrode is calculated, and the deflection angle of the liquid crystal molecules is finely adjusted to obtain molecular arrangement data with sub-pixel precision. Electric field diffusion and interaction analysis were performed on the sub-pixel precision molecular arrangement data and the electric field influence of surrounding pixels to obtain electric field interaction compensation data. The optimal driving voltage of each edge pixel is gradient-processed based on the electric field interaction compensation data to obtain the optimized voltage gradient distribution map. The optimized voltage gradient distribution map is converted into a sequence of control instructions that the LCD driver can recognize. These instructions are then arranged and organized according to the timing requirements of row scanning and column data transmission to output the electric field distribution control parameters.

[0029] Specifically, the original optimal voltage value is decomposed into several time segments, each containing different voltage levels and durations, forming a multi-stage driving waveform trajectory. These waveforms include initialization level, gradual rise level, hold level, and transition release level, collectively defining the orientation evolution of liquid crystal molecules throughout the refresh cycle, ensuring smoothness and linear response when transitioning from the initial alignment to the target state. Based on the multi-stage driving timing waveform data, the standard 128×128 driving circuit is extended in software, and a refined timing control system is built around the row and column driving architecture of the TFT panel. In the traditional 128×128 dot matrix structure, row driving activates each row in a sequential pulse scan, while column driving outputs the corresponding column voltage during each row activation period. To achieve multi-stage voltage control in this mechanism, each row scan cycle needs to be subdivided into multiple sub-time periods, and the column voltage output needs to be finely adjusted within each segment. Therefore, a data structure including time stamps, voltage stages, start and end signals, and switching rules is constructed for each row-column electrode intersection point, so that it can be dynamically loaded in the display controller to form programmable, pixel-specific timing control data. Based on the established control data, the potential evolution process at each row and column intersection is modeled. By comprehensively calculating the row scanning pulse, column voltage signal, and capacitance structure of the pixel electrode, the trajectory of electric field intensity change at each intersection within each time period is obtained. The change in the alignment angle of liquid crystal molecules is caused by this electric field trajectory. Therefore, by using the response curve from electric field to molecular deflection, the molecular alignment state of each edge pixel at sub-pixel resolution is calculated point by point, and the grayscale output or transmittance change of each pixel is predicted based on this data. Due to the close arrangement of pixels in the liquid crystal screen, there is mutual interference effect between the electric fields of pixels, especially in the edge region where edge diffusion, electric field superposition, and boundary effects are more likely to occur. Therefore, the molecular alignment data and the electric field state of neighboring pixels are incorporated into the coupling analysis framework. By constructing a local nine-square grid structure, the voltage control value, electric field distribution, and molecular response of the central pixel and its eight surrounding neighboring pixels are combined and analyzed to quantify the interference source that actually deviates from the ideal state of each pixel and represent it as electric field interaction compensation data. This data reflects the direct electric field influence of the surrounding environment on the pixel and reveals the display distortion or blurring trend caused by discontinuous edge regions. To effectively suppress the aforementioned interference and ensure natural character edge transitions, a gradient processing method is applied to the control voltage of each edge pixel, guided by compensation data. This involves not only adjusting a single value but also introducing voltage spatial transition logic across the entire pixel array, ensuring that the driving voltage changes between adjacent pixels exhibit a controllable, continuous, and smooth trend in both amplitude and direction. Gradient interpolation algorithms are then used to process the edge regions, smoothing the electric field diffusion boundaries and avoiding display defects such as brightness jumps, grayscale breaks, or jagged outlines. This optimization process generates a voltage gradient distribution map that incorporates spatial continuity characteristics.The optimization results are encapsulated into a sequence of control instructions that the LCD driver can recognize and parse. The encapsulation process follows a row-column drive control protocol, where each control instruction includes a row address identifier, column data sequence, timing control information, and voltage level flags. To meet the input requirements of the TFT driver chip, the instructions are organized row-by-row, with the loading order and timing of the corresponding column voltages specified in each row's data transmission. The system transmits these instruction data at high speed to the LCD controller within a fixed refresh cycle via a DMA interface, where the controller uniformly schedules and executes them, thereby creating the corresponding electric field modulation effect on the display panel.

[0030] In one specific embodiment, the process of performing step S5 may specifically include the following steps: An electric field coupling analysis was performed on the electric field influence between pixels of a 1.44-inch TFT LCD screen to obtain a coupling coefficient matrix describing the degree of electric field influence between the central pixel and the surrounding 8 pixels. Based on the coupling coefficient matrix, the target display electric field distribution is calculated in reverse and the reverse electric field is compensated to obtain the initial value of the compensated driving voltage distribution. Iterative optimization calculations are performed on the initial value of the compensated driving voltage distribution to obtain the final compensated driving voltage value; Based on the operating temperature range characteristics of the 1.44-inch TFT color LCD screen, the temperature-adaptive adjustment is performed on the final compensated driving voltage value to obtain the temperature-compensated driving voltage data. Linear interpolation optimization calculations are performed on the temperature-compensated driving voltage data to obtain optimized electric field compensation data. The system identifies the positions of all character edge pixels from subpixel-level precision character edge display data and applies optimized electric field compensation data to the corresponding character edge pixel positions to generate character display data with enhanced edge sharpness.

[0031] Specifically, this study analyzes the electric field coupling between pixels in a 1.44-inch TFT LCD screen. Pixels in a 1.44-inch TFT LCD screen are not completely independent entities; rather, they share a common electrode layout, electric field distribution, and charge migration path, leading to a certain degree of mutual interference at the visual level. This interference manifests primarily as the electric field generated by any pixel when a driving voltage is applied. This field not only acts on its own liquid crystal cell but also distributes with a certain intensity to neighboring pixels in its upper, lower, left, right, and diagonal positions, thus affecting the alignment of liquid crystal molecules in these areas. To quantify this interference and establish a coupling control model for display control, the electric field diffusion behavior under typical display conditions is measured or simulated during the experimental or modeling phase. This yields a nine-point coupling relationship based on the central pixel, constructing a coupling coefficient matrix containing the central pixel and its eight neighboring pixels. Each coefficient in this matrix represents the proportion of electric field interference caused by a specific neighboring pixel to the central pixel under standard voltage. The coupling coefficient of the central pixel itself should be set as the dominant value, while the coupling weights of the surrounding pixels are allocated according to spatial attenuation rules. Based on the coupling coefficient matrix, the target electric field distribution at the character edge is taken as a known value, and the driving voltage of each pixel under this distribution is calculated in reverse to accurately restore the required optical state while considering interference. This process constructs a reverse calculation model based on the target grayscale, electric field response curve, and coupling coefficient, and obtains a set of initial driving voltage distribution values ​​through matrix transformation and control equations. To improve the voltage control accuracy, the initial voltage data is iteratively optimized in multiple rounds. The optimization process aims to minimize visual error. In each round, the current voltage combination is substituted into the liquid crystal response model to simulate the deflection angle of the liquid crystal molecules and the final light transmittance distribution. Then, it is compared pixel by pixel with the ideal character edge grayscale distribution to obtain the error map and calculate the error gradient. Subsequently, the voltage of each pixel is adjusted based on the error direction, thereby continuously converging to the optimal driving voltage configuration with higher accuracy. This method relies on the liquid crystal response function built inside the system. This function considers the nonlinear deflection behavior of the actual liquid crystal material under different voltage gradients, as well as the physical characteristics such as the response delay of the driving electrode and the charging and discharging characteristics of the capacitor in the display, to ensure that each adjustment produces a stable response in practical applications. To ensure the optimization results are adaptable to actual operation under different environmental conditions, a temperature compensation mechanism is introduced to reprocess the optimized voltage data. Since the response speed, deflection stability, and electric field sensitivity of liquid crystal molecules all change with temperature, at low temperatures, the movement speed of liquid crystal molecules decreases and the driving threshold increases, while at high temperatures, the molecular response speed increases and the risk of electric field overshoot increases. These factors directly affect the clarity and edge sharpness of the displayed characters. To address this issue, the current operating temperature of the panel is collected in real time, and combined with the established temperature-response mapping table, the voltage of each pixel is adjusted accordingly.For example, the driving voltage is appropriately increased at low temperatures to enhance response, while it is appropriately decreased at high temperatures to prevent image blurring. Through temperature adaptive processing, the final voltage distribution is ensured to always be within the optimal operating window of the liquid crystal material. Temperature compensation is performed by linear interpolation optimization of the entire driving voltage space, especially for pixels with drastic voltage changes in edge regions. Due to the abrupt changes in the strokes of character edges, voltage steps occur in the pixel space. Directly applying these voltage values ​​can easily cause brightness jumps or jagged effects. Therefore, the continuity of the voltage difference between adjacent pixels in the driving voltage matrix is ​​judged. Without destroying the original character shape, voltage smoothing is performed using linear interpolation or bilateral filtering to generate optimized electric field compensation data with spatial transition continuity. This data manifests as a gradual grayscale change and consistent brightness at the pixel level. All edge pixel position information is extracted from the sub-pixel precision character display data, and the optimized electric field compensation data is precisely applied to these pixels according to the coordinate mapping method to form the final edge sharpness enhancement control dataset. This process combines features such as character stroke structure, stroke thickness, and overlapping areas to bind each edge information to its corresponding voltage gradient. The processing results are then organized into a driver instruction structure and uniformly sent to the LCD controller for execution. As the driving timing progresses, these specific pixels will exhibit a clear, sharp, and distinct visual appearance based on precisely controlled voltages. This enables the entire character structure to achieve high-fidelity display on a 1.44-inch TFT LCD screen, effectively eliminating the blurry edges, broken strokes, and jagged edges of traditional LCD characters. This improves the clarity, depth, and readability of small-sized devices in complex Chinese character display tasks.

[0032] In one specific embodiment, the process of performing step S6 may specifically include the following steps: Edge detection is performed on the character display data with enhanced edge sharpness to obtain a set of character edge pixels. Based on the set of character edge pixels, a local direction vector is calculated for each edge pixel to obtain edge direction feature data. Construct a direction-dependent filtering matrix based on edge direction feature data; For each edge pixel in the character edge pixel set, calculate the normalized distance of the ideal boundary, and convert the normalized distance into a transparency value through a cosine function to obtain edge transparency data; The character rendering process based on edge transparency data is divided into two stages: geometric correction and color blending. By fine-tuning the position with sub-pixel precision and blending the transparency of the foreground and background colors, anti-aliased pixel data is obtained. Based on different font sizes, the parameters of the anti-aliasing pixel data are adjusted to output clear Chinese character display data on a 1.44-inch TFT color LCD screen.

[0033] Specifically, edge detection is performed on the character display data with enhanced edge sharpness to identify locations in the character image where all grayscale values ​​change abruptly, i.e., the boundary regions between strokes and background. Edge extraction methods include gradient-based Sobel operators, edge template-based Prewitt methods, or multi-level intensity determination strategies based on Canny structures. The system selects the appropriate algorithm based on the computing power of the display controller. After edge detection, all pixels with obvious grayscale boundary features are extracted into a set of edge pixels, and the coordinate position of each edge pixel in the image space is preserved. Local structure analysis is performed on each identified edge pixel to calculate its edge direction in the character topology. This operation is achieved by establishing a small neighborhood window around the edge pixel, typically a 3×3 or 5×5 pixel block, and calculating the gradient direction vector within this region to obtain the local direction feature data of each edge pixel. Through the normalization of the direction vector and angle mapping, all edge pixels are assigned a clear direction attribute, and an edge angle map is established, thereby forming a structurally continuous and angularly accurate direction field in the image space. Based on the above direction features, the edge processing method is customized and optimized, i.e., a direction-dependent filtering matrix is ​​constructed. This filtering matrix, when performing image blurring and anti-aliasing operations, does not expand along a fixed direction, but rather determines the principal axis direction and expansion weight of the filtering kernel according to the direction vector of the current pixel. For example, for horizontally oriented edges, the filtering kernel is rotated and stretched to a horizontal shape, thereby enhancing the smoothness in the edge extension direction and reducing the blur diffusion in the vertical direction, thus eliminating jagged edges without compromising the clarity of the stroke edges. This direction-dependent filtering strategy is suitable for mixed strokes with various structures such as straight lines, diagonal lines, and hooks in complex Chinese characters. After constructing the filtering matrix, the distance between each edge pixel and its corresponding ideal boundary is calculated. The ideal boundary is obtained by fitting the stroke structure template, the stroke center line in the character dot matrix, or by inversely calculating the vector path reconstructed from the Bezier curve. The actual position of each edge pixel and its ideal contour are normalized to calculate a distance metric between 0 and 1, and this distance is converted into transparency data using a cosine function. The nonlinear characteristic of the cosine function—slow transparency change near the boundary and rapid decrease in transparency further away—is used to simulate the natural attenuation of light intensity during edge transitions. The closer the transparency value is to 1, the more foreground information the pixel should retain; the closer it is to 0, the more background information the pixel should blend. Based on the above edge transparency data, a two-stage processing mechanism is adopted in the final character rendering stage: first, geometric correction is performed, and then color mixing is performed. The goal of geometric correction is to fine-tune the physical position of edge pixels at sub-pixel precision, making them fit more accurately on the stroke outline, thereby reducing the boundary jump phenomenon caused by pixel granularity; this step combines the liquid crystal sub-pixel arrangement and gate spacing to correct the pixel position according to the horizontal or vertical micro-step size.In the color blending stage, the foreground and background colors are proportionally blended based on the transparency value of each edge pixel to generate a blended color value that conforms to the human visual model. This color value remains a complete pixel at the physical level, but in optical imaging, it approximates a continuous grayscale transition at the stroke edges, significantly improving boundary smoothness and the naturalness of character outlines. After basic anti-aliasing rendering, processing parameters are adjusted according to the font size of the current character. Different font sizes have different tolerances for edge details. At small font sizes, the number of pixels is limited, so edge contrast should be enhanced to improve readability. At large font sizes, the edge blur radius should be increased to improve smoothness and transition effects. Therefore, parameters such as the filter kernel size, transparency function slope, and direction vector weights are adjusted in different font size modes to adapt to actual application requirements. Meanwhile, considering the color response characteristics and pixel density of the 1.44-inch TFT color LCD screen, a sub-pixel arrangement strategy was adopted to combat the interference of jagged results. For example, horizontal RGB arrangement can cause color errors in certain tilted strokes. Therefore, a channel control and color space constraint mechanism was introduced into the color mixing algorithm to ensure that the final display effect is both clear and color accurate. After multiple processing steps, including orientation recognition, distance calculation, transparency mapping, color mixing, and font size adaptation optimization, a set of clear Chinese character pixel data with high coordination in geometric structure, color expression, and visual continuity is output. This data is then loaded into the LCD driver through a standard display control process, achieving high-precision Chinese character presentation under low-resolution conditions on the 1.44-inch TFT color LCD screen.

[0034] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0035] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0036] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for displaying characters on a liquid crystal module, characterized in that, include: Stroke decomposition and topological relationship establishment are performed on Chinese characters in the target character set to generate a character stroke dataset; Based on the character stroke dataset, perform two-dimensional character topology mapping and compression encoding on the characters to be displayed to form compressed character description data; The compressed character description data is input into the character reconstruction engine to perform hierarchical rendering and stroke synthesis to generate the target character dot matrix; The pixel array electric field distribution of the character edge portion in the target character dot matrix is ​​optimized, and the driving voltage gradient is controlled to obtain character edge display data with sub-pixel accuracy; Electric field compensation is performed on the character edge display data with subpixel precision to form character display data with enhanced edge sharpness; The character display data with enhanced edge sharpness is subjected to stroke-adaptive anti-aliasing processing to output clear Chinese character display data.

2. The character display method for a liquid crystal module according to claim 1, characterized in that, The process of decomposing Chinese characters in the target character set into strokes and establishing topological relationships to generate a character stroke dataset includes: The Chinese characters in the target character set are classified according to their stroke structure to obtain basic stroke classification results; Each basic stroke in the basic stroke classification result is plotted in a 128×128 pixel coordinate system to obtain a stroke dot matrix template. The Bezier curve algorithm is used to process the continuous curves of traditional strokes in the stroke dot matrix template to obtain an optimized stroke template. The optimized stroke template is subjected to pixel-level control to obtain recognizable stroke units in a micro-display environment, and each stroke in the recognizable stroke unit is assigned a unique identifier ID to obtain a set of basic strokes with identifiers. The storage format of the labeled basic stroke set is optimized, and the vector description data and pixel mapping data of all basic strokes are integrated to output a character stroke dataset.

3. The character display method for a liquid crystal module according to claim 1, characterized in that, The step of performing two-dimensional character topology mapping and compression encoding on the characters to be displayed based on the character stroke dataset to form compressed character description data includes: Based on the character stroke dataset, structural analysis is performed on the characters to be displayed to obtain character structure data. Then, based on the character structure data, the relative position information and connection information between strokes are recorded to obtain topological relationship data. The topological relationship data is encoded in a standardized format to obtain a topological relationship code. The standardized topological relation code is processed by the Burrows-Wheeler transform to obtain a compressed topological code sequence. A fine-tuning parameter set is introduced into the compressed topological coding sequence to accurately locate the position of each stroke within the character, thereby obtaining coded data with precise positioning information. The stroke ID sequence of the character to be displayed is combined with the encoded data containing precise positioning information to form compressed character description data.

4. The liquid crystal module character display method according to claim 1, characterized in that, The step of inputting the compressed character description data into the character reconstruction engine to perform hierarchical rendering and stroke synthesis to generate the target character dot matrix includes: A dual-level cache structure is designed in the character reconstruction engine, allocating 2KB RAM space for the stroke cache area and 14KB RAM space for the character cache area. The compressed character description data is subjected to stroke ID sequence extraction and topological relationship encoding to obtain complete character structure description information; Based on the complete description information of the character structure, the precise placement position of each stroke is calculated to obtain stroke positioning data; A stroke deformation algorithm is performed on the stroke positioning data to obtain a deformed stroke dot matrix, and the deformed stroke dot matrix is ​​combined according to topological relationships to obtain a synthesized complete character dot matrix; The dual-layer cache structure is used to manage the cache of the synthesized complete character dot matrix, and the target character dot matrix is ​​output.

5. The liquid crystal module character display method according to claim 1, characterized in that, The step of optimizing the pixel array electric field distribution of the character edge portion in the target character dot matrix and obtaining sub-pixel level precision character edge display data by controlling the driving voltage gradient includes: The pixel pitch, liquid crystal molecule type, alignment layer processing method and backlight characteristics of a 1.44-inch TFT LCD screen were extracted to obtain the physical characteristic parameters of the display screen. A nonlinear mapping function between pixel driving voltage and liquid crystal molecule arrangement angle is established based on the physical characteristic parameters of the display screen, and edge pixel distance data is calculated for the character edge pixels in the target character dot matrix. Based on the edge pixel distance data and the nonlinear mapping function, calculate the optimal driving voltage for each edge pixel; Based on the optimal driving voltage of each edge pixel, a precisely controlled electric field distribution is generated at the intersection of the row scanning electrode and the column data electrode, and the electric field distribution control parameters are obtained. A driving voltage matrix is ​​generated based on the electric field distribution control parameters, and the driving voltage value is transmitted to the display driving circuit through the optimized row and column scanning timing, outputting character edge display data with sub-pixel precision.

6. The liquid crystal module character display method according to claim 5, characterized in that, The optimal driving voltage for each edge pixel generates a precisely controlled electric field distribution at the intersection of the row scan electrode and the column data electrode, resulting in electric field distribution control parameters, including: The optimal driving voltage for each edge pixel is decomposed into timing data to obtain multi-stage driving timing waveform data. Based on the multi-stage driving timing waveform data, the standard 128×128 driving circuit is extended in software to construct a fine control mechanism for TFT-Gate driving and Source driving, and driving timing control data is obtained. Based on the driving timing control data, the potential distribution at the intersection of each row and column electrode is calculated, and the deflection angle of the liquid crystal molecules is finely adjusted to obtain molecular arrangement data with sub-pixel precision. The sub-pixel precision molecular arrangement data and the electric field influence of surrounding pixels are analyzed for electric field diffusion and interaction to obtain electric field interaction compensation data. Based on the electric field interaction compensation data, the optimal driving voltage of each edge pixel is gradient-processed to obtain an optimized voltage gradient distribution map. The optimized voltage gradient distribution map is converted into a control instruction sequence that can be recognized by the liquid crystal display driver, arranged and organized according to the timing requirements of row scanning and column data transmission, and output electric field distribution control parameters.

7. The liquid crystal module character display method according to claim 1, characterized in that, The electric field compensation is performed on the character edge display data based on the sub-pixel precision to form character display data with enhanced edge sharpness, including: An electric field coupling analysis was performed on the electric field influence between pixels of a 1.44-inch TFT LCD screen to obtain a coupling coefficient matrix describing the degree of electric field influence between the central pixel and the surrounding 8 pixels. Based on the coupling coefficient matrix, the target display electric field distribution is calculated in reverse and compensated in reverse to obtain the initial value of the compensated driving voltage distribution. Perform iterative optimization calculations on the initial value of the compensated driving voltage distribution to obtain the final compensated driving voltage value; Based on the operating temperature range characteristics of the 1.44-inch TFT color LCD screen, the temperature-adaptive adjustment is performed on the compensated final driving voltage value to obtain temperature-compensated driving voltage data. Linear interpolation optimization calculations are performed on the temperature-compensated driving voltage data to obtain optimized electric field compensation data. All character edge pixel positions are identified from the subpixel-level precision character edge display data, and the optimized electric field compensation data is applied to the corresponding character edge pixel positions to generate character display data with enhanced edge sharpness.

8. The liquid crystal module character display method according to claim 1, characterized in that, The process of performing stroke-adaptive anti-aliasing on the character display data with enhanced edge sharpness to output clear Chinese character display data includes: Edge detection is performed on the character display data with enhanced edge sharpness to obtain a set of character edge pixels. Based on the set of character edge pixels, a local direction vector is calculated for each edge pixel to obtain edge direction feature data. Construct a direction-dependent filtering matrix based on the edge direction feature data; For each edge pixel in the character edge pixel set, calculate the normalized distance of the ideal boundary, and convert the normalized distance into a transparency value through a cosine function to obtain edge transparency data; The character rendering process based on the edge transparency data is divided into two stages: geometric correction and color mixing. By fine-tuning the position with sub-pixel precision and mixing the transparency of the foreground and background colors, anti-aliasing pixel data is obtained. Based on different font sizes, the pixel data of the anti-aliasing process is adjusted to output clear Chinese character display data on a 1.44-inch TFT color LCD screen.

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