Industrial character image data enhancement method and related device

Abstract

The application provides an industrial character image data enhancement method and related equipment, decodes an obtained industrial character normal line image to be processed to obtain a normal line vector field of each pixel, performs mirror inversion and / or an angle measurement-based morphological operation on the normal line vector field to obtain an updated normal line vector field, encodes the updated normal line vector field to obtain an RGB image, stores the RGB image in an image matrix, and obtains an enhanced industrial character normal line image; compared with the prior art, the application directly performs vector field operation on the normal line image to realize controllable deformation of the character surface geometry, thereby generating an industrial character sample with rich morphological diversity, and solving the problems of fixed character geometry morphology, difficulty in simulating changes in concave-convex depth caused by process differences, edge blunting and other deformations in the prior art industrial character data generation method.

Description

Technical Field

[0001] This invention relates to the field of industrial character data processing technology, and in particular to a method and related equipment for enhancing industrial character image data. Background Technology

[0002] In the field of industrial character recognition, the geometric morphology of the character foreground is one of the key factors affecting the performance of recognition algorithms. Characters in industrial settings are typically formed on the surface of materials such as metal through processes like stamping, embossing, or engraving, presenting a raised or recessed three-dimensional structure. The visual representation of these characters depends not only on their basic outline but also significantly on variations in process parameters during actual manufacturing. For example, the magnitude of stamping pressure can cause changes in the depth of the character's protrusions and depressions, the degree of wear on the character mold can cause blunting or distortion of the character's edge shape, and mechanical vibrations during processing can cause slight deformations in the overall shape of the character. These geometric changes caused by process variations result in significant intra-class differences in the visual appearance of different instances of the same character category, posing a challenge to deep learning-based industrial character recognition.

[0003] Currently, data generation methods for industrial character recognition can be mainly divided into two categories. One category is the traditional method based on real image acquisition, which constructs a dataset by collecting a small number of workpiece images with characters in a specific industrial environment and manually annotating them. However, character data from the same batch of products are often highly similar, with only a few characters differing, making it difficult to cover the rich geometric changes caused by factors such as process fluctuations and mold wear. The other category is the synthesis method based on color space, such as rendering standard computer fonts and overlaying them onto a background image, or using techniques such as generative adversarial networks to generate text images. Although this type of method can generate a large amount of data, the generated character forms are usually limited to standardized font styles in computer font libraries, lacking the ability to simulate the three-dimensional geometric deformation features of real industrial characters caused by physical processing, such as depth changes, edge blunting, and local distortion.

[0004] In recent years, to simulate the three-dimensional geometric features of industrial characters, some existing technologies have begun to introduce surface normal maps for data synthesis. These methods typically estimate and extract the normal information of the character surface from real industrial images, and then generate a synthesized image through lighting rendering. However, this method of normal synthesis based on real-world extraction has significant limitations: (1) Fixed geometric shape: The extracted normal map records the geometric state of the character at the time of acquisition, and cannot simulate the geometric deformation such as blunting of character edges and change of depth caused by mold wear and changes in stamping pressure; (2) Strong data dependency: Building a normal library that covers all character categories requires collecting a large number of real artifacts, which is costly and difficult to cover long-tail characters; (3) Lack of vector space editing capabilities. Traditional image morphological transformations are mainly designed for grayscale or color space. If applied directly to normal maps, they will destroy the unit length constraint and geometric consistency of normal vectors, resulting in artifacts in the rendering lighting effect. Summary of the Invention

[0005] This invention provides an industrial character image data enhancement method and related equipment, which aims to solve the problems of fixed character geometry and difficulty in simulating deformations such as concave-convex depth changes and edge blunting caused by process differences in existing industrial character data generation methods.

[0006] To achieve the above objectives, the present invention provides an industrial character image data enhancement method, comprising: Step 1: Obtain the normal map of the industrial character to be processed. The normal map of the industrial character is used to characterize the three-dimensional geometric structure information of the surface of the industrial character. Step 2: Decode the industrial character normal map to obtain the normal vector field for each pixel; Step 3: Perform mirror inversion and / or morphological operations based on angle metrics on the normal vector field to obtain the updated normal vector field; Step 4: Encode the updated normal vector field to obtain an RGB image, and store the RGB image into an image matrix to obtain the enhanced industrial character normal map.

[0007] Furthermore, step 2 includes: For each pixel in the industrial character normal map, read the RGB channel value of the pixel to obtain the RGB channel value of each pixel; The RGB channel values ​​of each pixel are decoded to obtain the initial normal vector field of each pixel; Normalize the initial normal vector field to obtain the normal vector field.

[0008] Furthermore, normalizing the initial normal vector yields the expression for the normal vector field as follows: ; in, This represents the normalized normal vector field. Represents the initial normal vector field. These represent the components of the normal vector along the x-axis, y-axis, and z-axis, respectively.

[0009] Furthermore, step 3 includes: The normal vector field of each pixel is mirrored and inverted to obtain the inverted normal vector field; and / or Calculate the neighborhood range of each pixel in the industrial character normal map, and calculate the deviation angle between the normal vector field of each pixel in each neighborhood range and the Z-axis; Select the local optimal normal vector within the neighborhood based on the preset operation type; Based on the selected local optimal normal vector, spherical linear interpolation is performed on the normal vector field of each pixel to obtain the normal vector field after morphological transformation. The updated normal vector field is obtained from the inverted normal vector field and / or the normal vector field after morphological transformation.

[0010] Furthermore, the expression for calculating the neighborhood range of each pixel is: ; in, Indicates pixel position, Represents pixels The neighborhood range, Represents a structural element. Indicated by All pixels within the neighborhood of the center, Indicates the core size.

[0011] Furthermore, based on the preset operation type, the locally optimal normal vector is selected within the neighborhood, including: When the preset operation type is corrosion operation, the normal vector with the smallest deviation angle in the neighborhood is selected as the local optimal normal vector. When the preset operation type is dilation, the normal vector with the largest deviation angle in the neighborhood is selected as the local optimal normal vector.

[0012] Furthermore, by performing spherical linear interpolation on the normal vector field of each pixel based on the selected local optimal normal vector, the expression for the normal vector field after morphological transformation is obtained as follows: ; in, This represents the normal vector field after morphological transformation. This represents the spherical linear interpolation operation. This represents the selected local optimal normal vector. Represents pixels The normal vector field, This represents the angle between the normal vector field and the local optimal normal vector. Indicates the included angle threshold. This represents the interpolation coefficient.

[0013] The present invention also provides an industrial character image data enhancement device, comprising: The acquisition module is used to acquire the normal map of the industrial character to be processed. The normal map of the industrial character is used to characterize the three-dimensional geometric structure information of the surface of the industrial character. The decoding module is used to decode the normal map of industrial characters to obtain the normal vector field of each pixel; The operation module is used to perform mirror inversion and / or morphological operations based on angle metrics on the normal vector field to obtain an updated normal vector field. The encoding module is used to encode the updated normal vector field to obtain an RGB image, and then store the RGB image into an image matrix to obtain an enhanced industrial character normal map.

[0014] The present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement an industrial character image data enhancement method.

[0015] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements an industrial character image data enhancement method.

[0016] The above-described solution of the present invention has the following beneficial effects: This invention decodes the acquired industrial character normal map to obtain the normal vector field for each pixel; it then performs mirror inversion and / or morphological operations based on angle measurements on the normal vector field to obtain an updated normal vector field; finally, it encodes the updated normal vector field to obtain an RGB image, and stores the RGB image in an image matrix to obtain an enhanced industrial character normal map. Compared with existing technologies, this invention achieves controllable deformation of the character surface geometry by directly performing vector field operations on the normal map, thereby generating industrial character samples with rich morphological diversity. This solves the problems of fixed character geometry, difficulty in simulating deformations such as concavity and convexity depth changes and edge blunting caused by process differences in existing industrial character data generation methods.

[0017] Other beneficial effects of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the industrial character image data enhancement device in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the terminal device in an embodiment of the present invention. Detailed Implementation

[0019] To make the technical problems, solutions, and advantages of this invention clearer, a detailed description will be provided below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0020] In the description of this invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0021] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0022] This invention addresses existing problems by providing an industrial character image data enhancement method and related equipment.

[0023] like Figure 1 As shown, an embodiment of the present invention provides an industrial character image data enhancement method, comprising: Step 1: Obtain the normal map of the industrial character to be processed. The normal map of the industrial character is used to characterize the three-dimensional geometric structure information of the surface of the industrial character. Step 2: Decode the industrial character normal map to obtain the normal vector field for each pixel; Step 3: Perform mirror inversion and / or morphological operations based on angle metrics on the normal vector field to obtain the updated normal vector field; Step 4: Encode the updated normal vector field to obtain an RGB image, and store the RGB image into an image matrix to obtain the enhanced industrial character normal map.

[0024] In this embodiment of the invention, the character normal map to be processed is defined as follows: ,in, This indicates the size of the character normal map, which consists of multiple pixels, each storing RGB-encoded surface normal information.

[0025] Specifically, step 2 includes: For each pixel in the industrial character normal map, read the RGB channel value of the pixel to obtain the RGB channel value of each pixel; The RGB channel values ​​of each pixel are decoded to obtain the initial normal vector field of each pixel; Normalize the initial normal vector field to obtain the normal vector field.

[0026] Specifically, the RGB channel values ​​of each pixel are decoded to obtain the expression for the normal vector field of each pixel: ; ; ; ; in, Represents the initial normal vector field. These represent the components of the normal vector along the x-axis, y-axis, and z-axis, respectively. Represents the pixel value of the R channel. Represents the pixel value of the G channel. This represents the pixel value of the B channel. .

[0027] Specifically, normalizing the initial normal vector field yields the following expression for the normal vector field: ; in, Represents the normal vector field. This represents the initial normal vector field.

[0028] In this embodiment of the invention, mirroring and / or performing morphological operations based on angle measurements on the normal vector field is used to simulate different character surface geometric features.

[0029] Specifically, step 3 includes: By mirroring and reversing the normal vector field, we obtain the reversed normal vector field. and / or Calculate the neighborhood range of each pixel in the industrial character normal map, and calculate the deviation angle between the normal vector field of each pixel in each neighborhood range and the Z-axis; Select the local optimal normal vector within the neighborhood based on the preset operation type; Based on the selected local optimal normal vector, spherical linear interpolation is performed on the normal vector of each pixel to obtain the normal vector field after morphological transformation. The updated normal vector field is obtained from the inverted normal vector field and / or the normal vector field after morphological transformation.

[0030] In this embodiment of the invention, normal mirroring is a simple yet effective data augmentation operation used to generate samples with surface features opposite to the original text. The expression for the normal mirroring operation is: ; Comparing the above equation with the expression for the normal vector field, it can be seen that the normal mirroring inversion operation keeps the Z component unchanged while reversing the signs of the X and Y components. The geometric meaning of this operation is to reverse the tilt direction of the surface microstructure relative to the tangent plane. For convex regions, the original normal vector diverges outward along the edge; after reversal, the normal converges inward, creating a concave effect. For concave regions, the original normal converges inward; after reversal, it diverges outward, creating a convex effect. Keeping the Z component unchanged ensures that the angle between the normal and the surface reference plane remains unchanged, preserving the overall height distribution characteristics of the surface.

[0031] In the Blinn-Phong-based illumination model, the diffuse component passes through the light source direction. and normal direction dot product The specular reflection component is calculated through a half-vector. and normal direction dot product Calculate the specular intensity, where, , To determine the direction of observation, mirroring the normal changes the orientation of the local normal, causing the distribution of light and shadow and the position of highlights on the surface to be reversed under the same lighting and observation conditions. The raised edges that originally received strong light become shadow areas after the reversal, while the concave edges that were originally in shadow become highlight areas, thus creating a visual effect of interchange between raised and concave areas.

[0032] In this embodiment of the invention, the preset operation types include erosion operation and dilation operation. The erosion operation selects the normal vector with the smallest deviation angle from the Z-axis (i.e., the vertical direction of the surface) within the neighborhood, making the surface tend to be flat and the characters shrink inward. The dilation operation selects the normal vector with the largest deviation angle from the Z-axis within the neighborhood, making the surface tend to be steep and the characters expand outward.

[0033] Specifically, the expression for calculating the neighborhood range of each pixel is: ; in, Indicates pixel position, Represents pixels The neighborhood range, This invention defines a structural element. , Indicated by All pixels within the neighborhood of the center, Indicates the core size.

[0034] Specifically, the expression for calculating the deviation angle between the normal vector field of each pixel within each neighborhood and the Z-axis is: ; in, Indicates the deviation angle.

[0035] Specifically, based on the preset operation type, the locally optimal normal vector is selected within the neighborhood, including: When the preset operation type is erosion, the normal vector with the smallest deviation angle within the neighborhood is selected as the local optimal normal vector. The expression is: ; When the preset operation type is dilation, the normal vector with the largest deviation angle within the neighborhood is selected as the local optimal normal vector. The expression is: .

[0036] Specifically, based on the selected locally optimal normal vector, spherical linear interpolation is performed on the normal vector of each pixel to obtain the expression for the normal vector field after morphological transformation: ; in, This represents the normal vector field after morphological transformation. This represents the spherical linear interpolation operation. This represents the selected local optimal normal vector. Represents pixels The normal vector field, Let represent the angle between the normal vector field and the local optimal normal vector. In this embodiment of the invention, the expression for this angle is set as follows: , Indicates the included angle threshold. , Indicates the interpolation coefficients. .

[0037] In an embodiment of the present invention, if Then linear interpolation is used to avoid This leads to unstable interpolation results.

[0038] In this embodiment of the invention, the morphological transformation operation employs a parallel update strategy, i.e., the first... In the next iteration, the neighborhood lookup for all pixels is based on the first iteration. The result of the second iteration The calculated new normal vector field Temporarily stored in a separate buffer, and written back all at once after all pixels have been processed, to avoid the influence of pixel processing order on the result during sequential updates. After the next iteration, the updated normal vector field will be... Re-encoded as RGB image iteration After that, output the final normal map. Therefore, by adjusting the number of iterations... Dimensions of structural elements and interpolation coefficients The intensity and smoothness of morphological transformations can be adjusted.

[0039] Specifically, in this embodiment of the invention, the updated normal vector field is encoded to obtain an RGB image, and the RGB image is stored in an image matrix to obtain an enhanced industrial character normal map. This enhanced industrial character normal map is an industrial text normal map containing geometric deformation effects, which can be used in the subsequent lighting rendering module to generate industrial character image data with diverse geometric shapes, and then used for training the industrial character recognition network.

[0040] This invention decodes the acquired industrial character normal map to obtain the normal vector field for each pixel; performs mirror inversion and / or morphological operations based on angle measurements on the normal vector field to obtain an updated normal vector field; encodes the updated normal vector field to obtain an RGB image, and stores the RGB image in an image matrix to obtain an enhanced industrial character normal map; compared with the prior art, this invention achieves controllable deformation of the character surface geometry by directly performing vector field operations on the normal map, thereby generating industrial character samples with rich morphological diversity, solving the problems of fixed character geometry, difficulty in simulating deformations such as concavity and convexity depth changes and edge blunting caused by process differences in existing industrial character data generation methods.

[0041] Corresponding to the industrial character image data enhancement method described in the above embodiments, such as Figure 2 As shown, this embodiment of the invention also provides an industrial character image data enhancement device 100, which includes: The acquisition module 101 is used to acquire the normal map of the industrial character to be processed. The normal map of the industrial character is used to characterize the three-dimensional geometric structure information of the surface of the industrial character. Decoding module 102 is used to decode the normal map of industrial characters to obtain the normal vector field of each pixel; Operation module 103 is used to perform mirror inversion and / or morphological operations based on angle metrics on the normal vector field to obtain an updated normal vector field. The encoding module 104 is used to encode the updated normal vector field to obtain an RGB image, and store the RGB image into an image matrix to obtain an enhanced industrial character normal map.

[0042] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0043] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0044] This invention also provides a terminal device, such as... Figure 3 As shown, the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 3 The diagram shows only one processor, a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100. When the processor D100 executes the computer program D102, it implements the above-described industrial character image data enhancement method.

[0045] The terminal device D10 can be a desktop computer, laptop, handheld computer, server, server cluster, or cloud server, etc. This terminal device may include, but is not limited to, a processor D100 and a memory D101. Those skilled in the art will understand that... Figure 3 This is merely an example of terminal device D10 and does not constitute a limitation on terminal device D10. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0046] The processor D100 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0047] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may be an external storage device of the terminal device D10, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal device D10. Furthermore, the memory D101 may include both internal and external storage units of the terminal device D10. The memory D101 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory D101 can also be used to temporarily store data that has been output or will be output.

[0048] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0049] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0050] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements an industrial character image data enhancement method.

[0051] 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, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a building device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0052] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An industrial character image data augmentation method, characterized by, include: Step 1: Obtain the normal map of the industrial character to be processed. The normal map of the industrial character is used to characterize the three-dimensional geometric structure information of the surface of the industrial character. Step 2: Decode the industrial character normal map to obtain the normal vector field of each pixel; Step 3: Perform mirror inversion and / or morphological operations based on angle metrics on the normal vector field to obtain the updated normal vector field; Step 4: Encode the updated normal vector field to obtain an RGB image, and store the RGB image into an image matrix to obtain the enhanced industrial character normal map.

2. The industrial character image data augmentation method of claim 1, wherein, Step 2 includes: For each pixel in the industrial character normal map, the RGB channel value of the pixel is read to obtain the RGB channel value of each pixel; The RGB channel values ​​of each pixel are decoded to obtain the initial normal vector field of each pixel; The initial normal vector field is normalized to obtain the normal vector field.

3. The industrial character image data augmentation method of claim 2, wherein, Normalizing the initial normal vector yields the expression for the normal vector field as follows: ； wherein, represents a normal vector field, represents an initial normal vector field, respectively represent the components of the normal vector in the x-axis, Y-axis, and Z-axis directions.

4. The industrial character image data augmentation method of claim 1, wherein, Step 3 includes: The normal vector field of each pixel is mirrored and inverted to obtain the inverted normal vector field; Calculate the neighborhood range of each pixel in the industrial character normal map, and calculate the deviation angle between the normal vector field of each pixel in each neighborhood range and the Z-axis; Select the local optimal normal vector within the neighborhood according to the preset operation type; Based on the selected local optimal normal vector, spherical linear interpolation is performed on the normal vector field of each pixel to obtain the normal vector field after morphological transformation. The updated normal vector field is obtained from the inverted normal vector field and / or the normal vector field after morphological transformation.

5. The industrial character image data augmentation method of claim 4, wherein, The expression for calculating the neighborhood range of each pixel is: ； in, Indicates pixel position, Represents pixels The neighborhood range, Represents a structural element. Indicated by All pixels within the neighborhood of the center, Indicates the core size.

6. The industrial character image data enhancement method according to claim 4, characterized in that, Based on a preset operation type, a locally optimal normal vector is selected within the neighborhood, including: When the preset operation type is erosion operation, the normal vector with the smallest deviation angle in the neighborhood is selected as the local optimal normal vector. When the preset operation type is dilation operation, the normal vector with the largest deviation angle within the neighborhood is selected as the local optimal normal vector.

7. The industrial character image data enhancement method according to claim 4, characterized in that, Based on the selected local optimal normal vector, spherical linear interpolation is performed on the normal vector field of each pixel to obtain the expression of the normal vector field after morphological transformation: ； in, This represents the normal vector field after morphological transformation. This represents the spherical linear interpolation operation. This represents the selected local optimal normal vector. Represents pixels The normal vector field, This represents the angle between the normal vector field and the local optimal normal vector. Indicates the included angle threshold. This represents the interpolation coefficient.

8. An industrial character image data enhancement device, characterized in that, include: The acquisition module is used to acquire the normal map of the industrial character to be processed, wherein the normal map of the industrial character is used to characterize the three-dimensional geometric structure information of the surface of the industrial character; The decoding module is used to decode the industrial character normal map to obtain the normal vector field; The operation module is used to perform mirror inversion and / or morphological operations based on angle metrics on the normal vector field to obtain an updated normal vector field. The encoding module is used to encode the updated normal vector field to obtain an RGB image, and store the RGB image into an image matrix to obtain an enhanced industrial character normal map.

9. A terminal 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 computer program, it implements the industrial character image data enhancement method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1-9. When the computer program is executed by a processor, it implements the industrial character image data enhancement method as described in any one of claims 1 to 7.

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