An adaptive text display method and system for aiding memory

Abstract

This invention relates to the fields of computer vision, text processing, font generation, and adaptive learning, and specifically to an adaptive text display method and system for assisting memory. The method involves inputting text content to be memorized, which can be a text string and its corresponding vector font file, or a rasterized image containing the text content. Based on the input type, auxiliary display elements are generated or acquired to partially replace or cover the original text's visual form. Based on the user's memory proficiency index and / or the semantic features of the text content, several target layout rules are selected from a predefined layout rule library. According to the target layout rules, the original text data and the auxiliary display elements are logically fused to output the desired adaptive memory-assisting display interface. This invention elevates memory-assisting technology from a simple font transformation to a context-aware intelligent interactive interface, breaking the limitations of traditional font technology application scenarios.

Description

Technical Field

[0001] This invention relates to the fields of computer vision, text processing, font generation, and adaptive learning technologies, specifically to an adaptive text display method and system for assisting memory that can uniformly process vector fonts and rasterized text images. Background Technology

[0002] In the fields of memory assistance and text display, existing technologies have two prominent problems: First, existing font technologies are primarily designed for reading and printing, and their overall presentation cannot be altered to meet the needs of memorization. Furthermore, most existing font technologies are based on directly modifying and replacing vector font files (such as TrueType and OpenType). While this method can generate new font styles, its application is limited to the prerequisite of having "editable font files." For massive amounts of textual material existing in raster image form, such as scanned PDF documents, webpage screenshots, and text in photos, existing technologies cannot directly process them, severely limiting application scenarios and preventing the effective utilization of a large amount of potential learning material.

[0003] Second, existing text display methods are mostly static and monotonous. They typically offer a fixed, preset font distortion effect (such as overall dot-matrix). However, effective memory training is a dynamic process that requires dynamically adjusting the intensity and manner of prompts based on the user's increasing familiarity with the material. Furthermore, differentiated display strategies are needed for different semantic focuses (such as keywords versus function words) or to create varying intensities of cognitive interference. Current static methods cannot meet these personalized and adaptive needs, and the efficiency of memory assistance needs improvement.

[0004] Chinese patent document (Patent title: A font generation method, apparatus, electronic device and storage medium, Publication No.: CN119478135A, Publication Date: 2025.02.18) (referred to as: basic document) discloses an innovative font generation method that can generate auxiliary fonts such as dot-shaped and surface-shaped fonts through feature extraction and Boolean operations, providing basic glyph materials for memory assistance. However, its application scenarios are essentially limited to the "offline" rewriting of the font file itself. This patent fails to solve how to seamlessly apply this technology to rasterized image scenarios where font files are unavailable, nor does it construct a complete system that dynamically binds glyph materials with the user's real-time memory state and text semantic features, and makes intelligent typesetting decisions and rendering accordingly.

[0005] Therefore, there is an urgent need for a universal technical solution that can integrate both vector fonts and raster images as input sources, and can intelligently and dynamically adjust the display style according to the user's status and text content, so as to provide accurate and personalized memory assistance.

[0006] In view of this, the present invention is hereby proposed. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide an adaptive text display method and system for assisting memory. Building upon the technology of generating specific auxiliary fonts from basic documents, it further solves system-level problems such as multi-source input adaptation, dynamic rule decision-making, and intelligent rendering synthesis, constructing a complete memory assistance solution. Specifically: 1) It uniformly processes two input sources: vector font files and rasterized text images; 2) It intelligently selects and applies diverse typesetting rules based on user memory proficiency indicators and / or the semantic features of the text content; 3) Through innovative layer overlay and synthesis technology, it dynamically generates and outputs an adaptive memory assistance interface that integrates the original text and auxiliary display elements without modifying the original data (especially commercial font files). This invention achieves a significant leap from "font generation" to "intelligent display system" based on the basic document font generation method.

[0008] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides an adaptive text display method for assisting memory, comprising the following steps: S1. Input the text content to be memorized. The input format is a text string and the corresponding vector font file, or a rasterized image containing the text content. S2. Based on the input type, generate or obtain auxiliary display elements for partially replacing or covering the original text visual form through vector processing path or image processing path respectively; S3. Based on user memory proficiency indicators and / or semantic features of text content, select several target typesetting rules from a predefined typesetting rule library; S4. According to the target layout rules, the original text data and the auxiliary display elements are integrated and rendered in the display logic through layer overlay technology to output the required adaptive memory auxiliary display interface.

[0009] Specifically, in S2, when the input is a vector font file, the font generation method in the basic literature is used to directly generate the vector description data of the corresponding dot-shaped auxiliary font and surface-shaped auxiliary graphic font; When the input is a rasterized image, text detection and recognition are performed on the rasterized image to locate the text region, estimate the visual skeleton structure and key feature points of the characters in the text region; at the coordinate position of the key feature points, the stroke key feature points are extracted, a preset indicator symbol is drawn, and a dot-shaped auxiliary primitive sequence is generated, while a surface auxiliary graphic is generated based on the character bounding box.

[0010] Specifically, in S3, the typesetting rule base includes, but is not limited to, the following rules: Part-of-speech-based replacement rules: Perform grammatical analysis on the text content, identify content words and function words, and assign different display styles to words with different parts of speech; Progressive prompting rule: The visual density or fill area of ​​auxiliary display elements is dynamically adjusted according to the changes in the user's memory proficiency. The lower the memory proficiency, the more prompt information (auxiliary elements) there are. Multi-source interference rule: Mix and splice text fragments from different semantic sources in visual space and give them differentiated display styles to create high-intensity memory interference.

[0011] Furthermore, the specific implementation of the multi-source interference rule includes: A first visual fragment from at least one character from a first text source and a second visual fragment from at least one character from a second text source are spliced ​​and merged in visual space to form a composite display unit.

[0012] Specifically, in S4, the original text data and auxiliary display elements are logically integrated using layer overlay technology. This includes: rendering the original text data in a first logical layer; rendering the auxiliary display elements in a second logical layer; overlaying and merging the first and second logical layers; and controlling the visual integrity of the original text by adjusting the transparency, blending mode, or display range of the auxiliary display element layer.

[0013] Secondly, the present invention provides an adaptive text display system for assisting memory, comprising: The input parsing module is used to receive and determine whether the input source is a vector font file or a rasterized image; An element generation module, connected to the input parsing module, is used to generate auxiliary display elements based on the input type through a vector processing unit or an image processing unit. The rule decision module is used to receive user memory proficiency indicators and / or text semantic analysis results, and select target typesetting rules from the typesetting rule library; The rendering and compositing module is connected to the element generation module and the rule decision module respectively. It is used to blend and render the original text data with the auxiliary display elements through the layer overlay engine according to the target layout rules, and output the required adaptive memory auxiliary display interface.

[0014] Specifically, the layer overlay engine merges and renders the original text data with the auxiliary display elements by controlling the first logical layer to render the original text data, controlling the second logical layer to render the auxiliary display elements, and then overlaying and compositing the first and second logical layers.

[0015] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the adaptive text display method as described above.

[0016] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the adaptive text display method described above.

[0017] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects: 1. Strong input versatility: This invention innovatively designs a parallel vector and image dual processing path, unifying the processing methods of the two most important text digital forms (editable text and image text), completely breaking the limitations of traditional font technology application scenarios, and enabling the adaptive text display system to process text materials from any source.

[0018] 2. Technological Inheritance and Systematization Improvement: The invention directly and efficiently utilizes mature font generation methods from foundational literature within the vector processing path, ensuring the quality and consistency of core glyph generation. Simultaneously, this invention constructs a complete front-end input adaptation, mid-end intelligent decision-making, and back-end rendering output system around this core, enabling the innovative achievements of foundational literature to maximize their value within a more powerful and practical system framework.

[0019] 3. Innovative and efficient memory interference model: The proposed "multi-source interference rule," especially the character visual fragment splicing and fusion technology, goes beyond the simple deformation of a single character. Through the deep interweaving of visual information across text and characters, it creates a strong cognitive conflict, more effectively stimulating deep processing and active recall in the brain. It is suitable for high-intensity memory consolidation training and has significant effects.

[0020] 4. Intelligent and Adaptive Decision-Making: By introducing a rule-based decision-making module and a layout rule library containing multiple rule types, the static text display is upgraded to a dynamic "memory training engine." The system can automatically match the most suitable display strategy based on the user's real-time memory status (proficiency) and the inherent characteristics (semantics) of the text, achieving truly personalized learning.

[0021] 5. High Copyright Compatibility: This invention employs an innovative logical layer overlay technology for final synthesis. For copyrighted commercial fonts, the adaptive text display system only needs to render the original text data at the bottom layer and independently render auxiliary display elements at the top layer, achieving the display effect through visual overlay. The entire process requires no modification to the font file itself, thus avoiding copyright risks and greatly expanding its practical application. Attached Figure Description

[0022] The accompanying drawings are incorporated in and form part of this specification, and together with the description serve to explain the principles of the invention.

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a block diagram of the overall architecture of the system of the present invention; Figure 2 This is the overall flowchart of the method of the present invention; Figure 3 This is a comparison diagram of the processing paths in vector mode and image mode; Figure 4 These are schematic diagrams illustrating the effects of different formatting rules (part-of-speech replacement, progressive hints, and multi-source interference); Figure 5 This is a schematic diagram illustrating the principle of layer overlay rendering; Figure 6 This is a schematic diagram illustrating the processing flow and effects from the original rasterized image to the composite effect in image mode; Figure 7 This is a comparative analysis chart showing the effects of applying multiple mixed typesetting rules; Figure 8 This is a schematic diagram illustrating the specific implementation of character fragment splicing and fusion in the "multi-source interference rule". Detailed Implementation

[0025] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples consistent with some aspects of the invention as detailed in the appended claims.

[0026] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0027] This invention provides an adaptive text display method for assisting memory, comprising the following steps: S1. Input the text content to be memorized. The input format is a text string and the corresponding vector font file, or a rasterized image containing the text content. S2. Based on the input type, generate or obtain auxiliary display elements for partially replacing or covering the original text visual form through vector processing path or image processing path respectively; S3. Based on user memory proficiency indicators and / or semantic features of text content, select several target typesetting rules from a predefined typesetting rule library; S4. According to the target layout rules, the original text data and the auxiliary display elements are integrated and rendered in the display logic through layer overlay technology to output the required adaptive memory auxiliary display interface.

[0028] Specifically, in S2, when the input is a vector font file, the font generation method in the basic literature is used to directly generate the vector description data of the corresponding dot-shaped auxiliary font and surface-shaped auxiliary graphic font; When the input is a rasterized image, text detection and recognition are performed on the rasterized image to locate the text region, estimate the visual skeleton structure and key feature points of the characters in the text region; at the coordinate position of the key feature points, the stroke key feature points are extracted, a preset indicator symbol is drawn, and a dot-shaped auxiliary primitive sequence is generated, while a surface auxiliary graphic is generated based on the character bounding box.

[0029] Specifically, in S3, the typesetting rule base includes, but is not limited to, the following rules: Part-of-speech-based replacement rules: Perform grammatical analysis on the text content, identify content words and function words, and assign different display styles to words with different parts of speech; Progressive prompting rule: The visual density or fill area of ​​auxiliary display elements is dynamically adjusted according to the changes in the user's memory proficiency. The lower the memory proficiency, the more prompt information (auxiliary elements) there are. Multi-source interference rule: Mix and splice text fragments from different semantic sources in visual space and give them differentiated display styles to create high-intensity memory interference.

[0030] Furthermore, the specific implementation of the multi-source interference rule includes: A first visual fragment from at least one character from a first text source and a second visual fragment from at least one character from a second text source are spliced ​​and merged in visual space to form a composite display unit.

[0031] Specifically, in S4, the original text data and auxiliary display elements are logically integrated using layer overlay technology. This includes: rendering the original text data in a first logical layer; rendering the auxiliary display elements in a second logical layer; overlaying and merging the first and second logical layers; and controlling the visual integrity of the original text by adjusting the transparency, blending mode, or display range of the auxiliary display element layer.

[0032] This invention provides an adaptive text display system for assisting memory, comprising: The input parsing module is used to receive and determine whether the input source is a vector font file or a rasterized image; An element generation module, connected to the input parsing module, is used to generate auxiliary display elements based on the input type through a vector processing unit or an image processing unit. The rule decision module is used to receive user memory proficiency indicators and / or text semantic analysis results, and select target typesetting rules from the typesetting rule library; The rendering and compositing module is connected to the element generation module and the rule decision module respectively. It is used to blend and render the original text data with the auxiliary display elements through the layer overlay engine according to the target layout rules, and output the required adaptive memory auxiliary display interface.

[0033] Specifically, the layer overlay engine merges and renders the original text data with the auxiliary display elements by controlling the first logical layer to render the original text data, controlling the second logical layer to render the auxiliary display elements, and then overlaying and compositing the first and second logical layers.

[0034] To more clearly illustrate the features of this invention, the following explanation is provided.

[0035] This invention provides an overall adaptive text display system architecture and workflow, as detailed below: See Figure 1 and Figure 2 As shown, the adaptive text display system of this invention can be based on electronic devices such as servers, personal computers, tablets, or smartphones. Logically, the system mainly includes an input layer, a processing layer, a rule base, an output layer, and a feedback loop.

[0036] See Figure 2 As shown, the workflow begins with the user submitting the text content to be remembered (S201). The input parsing module (S202) then judges the input: If the input is "text string and vector font file" (such as text selected by the user in word processing software), the vector mode processing path will be entered; If the input is a "rasterized image" (such as a screenshot or scan containing text), the image mode processing path is entered; the ultimate goal of both paths is to generate auxiliary display elements S203 that can be used for subsequent rendering.

[0037] Simultaneously, the system acquires or calculates the user's memory proficiency index S204 and performs semantic feature analysis on the text content S205. The rule decision module S206 integrates these two types of information and selects one or more "target typesetting rules" from the "typesetting rule base".

[0038] Subsequently, the rendering and compositing module, according to the selected rules, overlays and blends the original text data with the generated auxiliary display elements (S207), ultimately outputting an adaptive memory auxiliary display interface (S208) for user use. User interaction and feedback data generated during use (such as test scores and dwell time) are collected by the system for memory proficiency assessment (S209). The assessment results update the user's memory status indicators, thus forming a closed-loop feedback adjustment system (S210) to achieve true adaptive learning.

[0039] This invention provides a dual-modal processing path for vector mode and image mode, and the specific implementation process is as follows: See Figure 3 As shown, this embodiment focuses on illustrating the differences in the core processing path.

[0040] 1. Vector mode processing path (left path): This path is used when the system determines that the input is an editable text and font file (such as "SimSun" text obtained from Word).

[0041] S301: Parse the text string and font information to obtain the vector outline data of each character.

[0042] S302: Invoke the font generation method described in detail in the basic literature, which includes: S3021: Extract the boundary feature lines of the font outline (i.e., the minimum bounding rectangle). S3022: Obtain key feature points (starting point, turning point, intersection point, etc.) of each character stroke through font extraction feature model; S3023: Generate dotted auxiliary fonts based on feature points (replacing outlines with indicator symbols); S3024: Generate surface-based auxiliary graphic fonts based on boundary feature line meshing.

[0043] S303 / S304: Generate vector-described point-based auxiliary fonts and area-based auxiliary graphic fonts, respectively. These generated products are complete collections of vector objects with font attributes, which can be stored and used independently.

[0044] S305: At this point, the "auxiliary display elements" output by the vector mode path are vectorized, which has the advantages of infinite scaling without distortion and flexible editing.

[0045] 2. Image mode processing path (right path): When the input is an image containing text (e.g.) Figure 6 (As shown in the top left corner), follow this path.

[0046] S306: Preprocess the image (such as noise reduction and binarization), and then use OCR (Optical Character Recognition) technology to detect and recognize text to determine which characters are contained in the image.

[0047] S307: Locates text regions and character segments, precisely defining the pixel position of each character in the image.

[0048] S308: For each segmented character image block, its visual skeleton structure is estimated using image processing algorithms (such as skeletonization and contour analysis); this step aims to simulate the understanding of stroke structure in vector mode.

[0049] S309: Extract key feature points (such as skeleton endpoints, intersections, and inflection points) from the estimated visual skeleton. These points correspond to the beginning, end, and turning points of written strokes.

[0050] S310 / S311: Based on the extracted feature points, draw preset indicator symbols (such as circles or squares) at the corresponding pixel coordinate positions to form a rasterized dot-shaped auxiliary primitive sequence; at the same time, based on the character bounding box obtained in S307, generate a grid and selectively fill it to form a rasterized surface-shaped auxiliary graphic.

[0051] S312: The "auxiliary display elements" output by the image mode path are rasterized layers (such as...). Figure 6 The image consists of point-like auxiliary layers and area-like auxiliary layers, which are aligned with the pixels of the original image layer and synthesized using techniques such as alpha blending to obtain the final output effect.

[0052] After the auxiliary display element is ready (S313), the two paths converge into a common subsequent process.

[0053] This invention provides application examples of a typesetting rule base, as detailed below: See Figure 4 , Figure 7 The typesetting rule base of the present invention contains a variety of rules that can be used individually or in combination.

[0054] 1. Part-of-speech-based replacement rules: The system integrates Natural Language Processing (NLP) components to perform syntactic analysis on the input text. For example... Figure 4As shown in the upper part, for the sentence "The weather is nice today. Let's go for a walk in the park.", the system identifies "today", "weather", "nice", "we", "go", "park", and "walk" as content words (nouns, verbs, adjectives), while words like "very", ",", "together", etc. are function words or punctuation marks. According to the rule settings, content words are displayed in full fonts to retain core information, while function words, some punctuation marks, or characters around function words are displayed in dotted auxiliary fonts. In this way, when users recall, they need to focus on mastering content words, and function words are connected through dotted prompts, which conforms to the language memory rule.

[0055] 2. Progressive Prompt Rule: This rule is related to the user's strong memory proficiency. The system maintains the user's memory file, and the proficiency can be divided into levels such as "strange", "familiar", "skilled", "proficient", etc.; as Figure 4 As shown in the middle part: Strange: Provide high-density prompts, with 100% of the original information displayed. It may only show dot-like feature points, or the area of surface filling is very small, the degree of missing is small, and the promptness is strong.

[0056] Familiar: Reduce the prompt intensity, with 70% of the original information displayed. For example, increase the filling area of the surface auxiliary graph to 30%, revealing part of the original glyph.

[0057] Skilled: Further reduce the prompts, with 50% of the original information displayed. The area of surface filling increases to 50%, and the glyph is more complete.

[0058] Proficient: Almost no prompts are needed, and some original information can still be retained, such as 20% of the original information displayed. It may use high-density dot-like fonts (such as 80% of the feature points retained) as mild interference in the final stage.

[0059] Through this rule, the adaptive text display system realizes the personalized dynamic adjustment of the learning path.

[0060] 3. Multi-source Interference Rule: This is the highest-level training mode, such as Figure 4 shown in the lower part and Figure 8 The system selects characters from different source texts (such as Tang poetry A and Tang poetry B) and processes them separately.

[0061] S801: Select source texts, such as the character "spring" in "Spring Morning" and the character "quiet" in "Thoughts in the Silent Night".

[0062] S802 / S803: Decompose each character; for example, divide the character "spring" into upper and lower parts vertically, and divide the character "quiet" into upper and lower parts in the same way.

[0063] S804 / S805: Apply different visual processing to the segmented fragments; for example, keep the upper part of the character "春" normally displayed, while covering the lower part with a planar graphic of the same color as the background; at the same time, cover the upper part of the character "静" and keep the lower part normally displayed.

[0064] S806: Piece together and fuse the processed fragments, that is, precisely align and piece together the upper part of the character "春" and the lower part of the character "静" vertically to form a synthetic character that seemingly looks complete but is actually composed of two different characters pieced together.

[0065] S807 / S808: Post-processing such as edge fusion and color unification can be performed to make the visual effect of the synthetic character more natural, but the semantic interference is extremely strong.

[0066] S809: Synthetic display effect: As Figure 8 shown in the character-level demonstration at the bottom, after the original characters "春" and "静" are processed, their fragments are pieced together to form a brand-new graphic; this deep interweaving strongly forces the brain to abandon the overall glyph prediction and instead focus on local feature recognition and context logical reasoning, with extremely high memory training value.

[0067] The present invention provides a layer overlay rendering synthesis technology, specifically as follows: Refer to Figure 5 As shown, the core of the rendering synthesis module is the layer overlay engine.

[0068] The system constructs at least two logical layers: The first logical layer (bottom layer) is used to render the original text data; the second logical layer is used to render auxiliary display elements (dot-shaped, planar or fragmented).

[0069] Each logical layer has independent attribute configurations, including transparency (Alpha value), blending mode (such as normal, multiply, screen, etc.), and position information.

[0070] Synthesis process: The rendering engine synthesizes the two logical layers at the pixel level according to preset rules. For example, by reducing the transparency of the second logical layer, the original text part can be made to show through, achieving continuous adjustment of "visual integrity". Through specific blending modes, more rich visual effects can be produced between the auxiliary elements and the original background.

[0071] As Figure 5 an example, the original text "记忆" is rendered in the first logical layer; the dot-shaped and planar auxiliary elements are rendered in the second logical layer and the third logical layer (which can be regarded as a sub-layer or different channels of the second logical layer); the final synthetic effect is the result of the fusion of the three, and the original glyphs are partially replaced and covered, forming a unique adaptive memory auxiliary display interface.

[0072] The significant advantage of this technology is that, for commercial fonts, the original text layer is generated by the normal font rendering engine of the operating system or application software. This invention only needs to overlay a semi-transparent auxiliary element layer on top of it to achieve the desired effect, without touching or modifying the font file itself, which is legal and compliant. Example

[0073] This embodiment provides a hybrid rule application and effect evaluation method for an adaptive text display method used to assist memory, as detailed below: See Figure 7 As shown, in practical applications, rules can be used in combination to achieve more refined or stronger training effects: The system takes the original text "Artificial intelligence technology is changing the world" as an example and applies rules based on the user's memory proficiency being "medium" and the text type being "technology".

[0074] Single rule effect: Applying part-of-speech substitution, progressive hints (dot density 60%), and multi-source interference rules respectively produces different visual deformation effects.

[0075] Double / triple blending rule effects: Figure 7 The demonstration showcased the effects of "part-of-speech replacement + progressive hints," "part-of-speech replacement + multi-source interference," "progressive hints + multi-source interference," and the fusion of all three. It can be seen that the visual complexity of the interface, the intensity of memory interference, and the amount of information contained in the hints generated by the hybrid rules increase progressively.

[0076] Comparative analysis of effects: Figure 7 The following section provides a quantitative comparison of the effects. A single rule is suitable for beginner training, a dual rule for intermediate training, while a triple rule, which combines multiple interferences and substitutions, creates an extremely high cognitive load, suitable for advanced memory consolidation and challenges. The system can recommend or automatically apply rule combinations of varying complexity based on the user's goals and current state.

[0077] In summary, this invention elevates memory assistance technology from a simple "font transformation" to a "context-aware intelligent interactive interface" by constructing a complete system integrating dual-mode input processing, intelligent rule-based decision-making, and advanced layer rendering. It not only technically supports a wider range of data sources but also provides more refined, adaptive, and powerful training tools at the cognitive science level, possessing extremely high practical value and market potential.

[0078] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention.

[0079] It should be understood that the present invention is not limited to the content already described above, and various modifications and changes can be made without departing from its scope. The scope of the present invention is limited only by the appended claims.

Claims

1. An adaptive text display method for assisting memory, characterized in that, Includes the following steps: S1. Input the text content to be memorized. The input can be a text string and the corresponding vector font file, or a rasterized image containing the text content. S2. Based on the input type, generate or obtain auxiliary display elements for partially replacing or covering the original text visual form; S3. Based on user memory proficiency indicators and / or semantic features of text content, select several target typesetting rules from a predefined typesetting rule library; S4. According to the target layout rules, the original text data and the auxiliary display elements are integrated in the display logic to output the required adaptive memory auxiliary display interface.

2. The adaptive text display method according to claim 1, characterized in that, The generation or acquisition of auxiliary display elements based on input type includes: When the input is a vector font file, vector descriptions of the corresponding dot-shaped auxiliary fonts and area-shaped auxiliary graphic fonts are generated based on the font generation method. When the input is a rasterized image, text detection and recognition are performed on the rasterized image to locate the text region, and point-like auxiliary primitive sequences and area-like auxiliary graphics are directly generated based on the text region.

3. The adaptive text display method according to claim 2, characterized in that, The dotted auxiliary primitive sequence includes: estimating the visual skeleton structure and key feature points of characters in the text region; and drawing preset indicator symbols at the coordinate positions of the key feature points.

4. The adaptive text display method according to claim 1, characterized in that, The typesetting rule base includes at least one of the following rules: Part-of-speech-based replacement rules: Perform grammatical analysis on the text content, identify content words and function words, and assign different display styles to words with different parts of speech; Progressive prompting rule: Dynamically adjust the visual density or fill area of ​​auxiliary display elements according to changes in the user's memory proficiency; Multi-source interference rule: Mix and splice text fragments from different semantic sources in visual space and give them differentiated display styles.

5. The adaptive text display method according to claim 4, characterized in that, The specific implementation of the multi-source interference rule includes: A first visual fragment from at least one character from a first text source and a second visual fragment from at least one character from a second text source are spliced ​​and merged in visual space to form a composite display unit.

6. The adaptive text display method according to claim 1, characterized in that, The original text data and auxiliary display elements are logically integrated using layer overlay technology, including: Render the original text data in the first logical layer; Render auxiliary display elements in the second logical layer; The first logical layer and the second logical layer are overlaid and merged, wherein the auxiliary display elements at least partially determine the visual presentation completeness of the original text data.

7. An adaptive text display system according to any one of the adaptive text display methods described in claims 1-6, characterized in that, include: The input parsing module is used to receive and determine whether the input source is a vector font file or a rasterized image; The element generation module is used to generate or obtain auxiliary display elements based on the input type. The rule decision module is used to select a target typesetting rule from the typesetting rule base based on the user's memory proficiency index and / or the semantic features of the text content. The rendering and compositing module is used to merge the original text data with auxiliary display elements according to the target layout rules, and output the required adaptive memory auxiliary display interface.

8. The adaptive text display system according to claim 7, characterized in that, The rendering and compositing module uses layer overlay technology to fuse the original text data with the auxiliary display elements. It is configured to: control the first logical layer to render the original text data, control the second logical layer to render the auxiliary display elements, and overlay and composite the first logical layer and the second logical layer.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the adaptive text display method as described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the adaptive text display method as described in any one of claims 1-6.

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