Math problem solving method and system incorporating visual scanning recognition and correction

Abstract

The application provides a mathematical problem solving method and system combining visual scanning identification and correction, which adjusts the state of secondary scanning shooting based on the first target object picture obtained by primary scanning shooting, so as to obtain a picture without picture content defects about the target object; then the mathematical problem text is extracted from the picture obtained by secondary scanning shooting, and text correction and text keyword identification are performed, so as to ensure the correctness of the mathematical problem text and the matching of the selected problem solving model, reduce the interference of the content defects of the mathematical problem text on the problem solving model, and improve the problem solving accuracy and reliability of the problem solving model on the mathematical problem text.

Description

Technical Field

[0001] This invention relates to the field of intelligent computing, and in particular to a mathematical problem-solving method and system that combines visual scanning recognition and correction. Background Technology

[0002] In the field of deep learning, deep learning models based on co-training, such as MAWPS or Math23k, can solve elementary school-level math problems with over 81% accuracy, while deep learning models combining co-training and graph neural networks can solve university-level math problems with 95% accuracy. Current technologies focus on improving the accuracy of deep learning models in solving math problems, without considering how to adjust the input data, such as image data, which may be blurry or incomplete. Directly inputting flawed math problem data into a deep learning model will reduce the accuracy and reliability of its solutions. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a mathematical problem-solving method and system that combines visual scanning recognition and correction. Based on a first image of the target object obtained from an initial scan, the system obtains information on the presence of characters in the image, and adjusts this information for a second scan. The text of the second image of the target object obtained from the second scan is converted into mathematical problem text, which is then corrected and subject to text keyword recognition to determine the problem's attribute information, thereby selecting a matching problem-solving model. The problem-solving model then processes the mathematical problem text to obtain a solution. Using the first image of the target object obtained from the initial scan as a reference, the system adjusts the state of the second scan to obtain a clear image of the target object without any content defects. Finally, the mathematical problem text is extracted from the second image, and text correction and text keyword recognition are performed to ensure the correctness of the mathematical problem text and the matching of the selected problem-solving model. This reduces the interference of the mathematical problem text's own content defects on the problem-solving model, improving the accuracy and reliability of the problem-solving model in solving mathematical problem texts.

[0004] This invention provides a mathematical problem-solving method that combines visual scanning recognition and correction, comprising the following steps:

[0005] Step S1: After the initial scan and capture of the target object, the obtained first target object image is processed for recognition to obtain the image character presence status information; based on the image character presence status information, the capture state for the second scan and capture of the target object is adjusted.

[0006] Step S2: Perform text conversion processing on the second target object image obtained by the secondary scanning to generate the math problem text corresponding to the second target object image; perform inspection processing on the math problem text, and perform correction processing on the math problem text based on the inspection processing result;

[0007] Step S3: Perform text keyword recognition on the corrected math problem text to obtain the problem attribute information corresponding to the math problem text; select a matching problem-solving model based on the problem attribute information;

[0008] Step S4: Input the corrected math problem text into the problem-solving model to obtain the corresponding solution.

[0009] Further, in step S1, after the initial scan and capture of the target object, the obtained first target object image is processed for recognition to obtain the image character presence status information; based on the image character presence status information, the capture state for the second scan and capture of the target object is adjusted, including:

[0010] The target object is scanned and photographed in line-by-line mode to obtain a first target object image containing the visual content of each line region of the target object; each line region on the first target object image is identified and processed to obtain the character outline feature information of each line region, which is used as the character existence status information of the image.

[0011] Based on the character outline feature information, determine whether the characters in the corresponding row area of ​​the first target object image have blurred or overlapping outlines; if so, determine the position information of the corresponding row area on the target object.

[0012] During the secondary scanning and shooting of the target object, the focus state for shooting the visual content of the corresponding row area of ​​the target object is adjusted according to the position information.

[0013] Further, in step S2, the second target object image obtained by the secondary scanning is subjected to text conversion processing to generate mathematical problem text corresponding to the second target object image; the mathematical problem text is checked, and based on the result of the checking, the mathematical problem text is corrected, including:

[0014] After performing depth-of-field consistency processing on all row areas of the second target object image obtained by the secondary scanning, the second target object image is subjected to text recognition and conversion processing to generate the text content contained in the global image of the second target object.

[0015] The text content is subjected to semantic recognition processing to obtain the math problem text corresponding to the second target object image;

[0016] The mathematical problem text is subjected to grammar and spelling detection processing, thereby correcting the grammar and spelling errors in the mathematical problem text.

[0017] Further, in step S3, the corrected math problem text undergoes text keyword recognition to obtain the problem attribute information corresponding to the math problem text; based on the problem attribute information, a matching problem-solving model is selected, including:

[0018] Extract all mathematical symbols present in the corrected mathematical problem text, and obtain the problem attribute information corresponding to the mathematical problem text based on the mathematical symbols with the highest frequency of occurrence.

[0019] Based on the question attribute information, a matching problem-solving model is selected from the problem-solving model library.

[0020] Furthermore, in step S4, the corrected mathematical problem text is input into the problem-solving model to obtain the corresponding solution results, including:

[0021] After optimizing and training the problem-solving model, the corrected mathematical problem text is input into the problem-solving model to obtain the corresponding problem-solving results.

[0022] This invention also provides a mathematical problem-solving system that combines visual scanning recognition and correction, including:

[0023] The image capture and analysis module is used to perform initial scanning and capture of the target object, identify and process the obtained first target object image, and obtain the image character presence status information of the first target object image; and adjust the capture status of the second scanning and capture of the target object based on the image character presence status information.

[0024] The math problem text generation module is used to perform text conversion processing on the second target object image obtained by the secondary scanning to generate math problem text corresponding to the second target object image; to perform inspection processing on the math problem text; and to perform correction processing on the math problem text based on the inspection processing results.

[0025] The math problem-solving model determination module is used to perform text keyword recognition on the corrected math problem text to obtain the problem attribute information corresponding to the math problem text; and select a matching problem-solving model based on the problem attribute information.

[0026] The math problem-solving execution module is used to input the corrected math problem text into the problem-solving model and obtain the corresponding solution result;

[0027] Furthermore, after the image capturing and analysis module performs an initial scan and capture of the target object, it performs recognition processing on the obtained first target object image to obtain the image character presence status information; based on the image character presence status information, it adjusts the capturing state for a second scan and capture of the target object, including:

[0028] The target object is scanned and photographed in line-by-line mode to obtain a first target object image containing the visual content of each line region of the target object; each line region on the first target object image is identified and processed to obtain the character outline feature information of each line region, which is used as the character existence status information of the image.

[0029] Based on the character outline feature information, determine whether the characters in the corresponding row area of ​​the first target object image have blurred or overlapping outlines; if so, determine the position information of the corresponding row area on the target object.

[0030] During the secondary scanning and shooting of the target object, the focus state for shooting the visual content of the corresponding row area of ​​the target object is adjusted according to the position information.

[0031] Furthermore, the math problem text generation module performs text conversion processing on the second target object image obtained from the secondary scanning to generate math problem text corresponding to the second target object image; the math problem text is then checked, and based on the results of the checking, the math problem text is corrected, including:

[0032] After performing depth-of-field consistency processing on all row areas of the second target object image obtained by the secondary scanning, the second target object image is subjected to text recognition and conversion processing to generate the text content contained in the global image of the second target object.

[0033] The text content is subjected to semantic recognition processing to obtain the math problem text corresponding to the second target object image;

[0034] The mathematical problem text is subjected to grammar and spelling detection processing, thereby correcting the grammar and spelling errors in the mathematical problem text.

[0035] Furthermore, the math problem-solving model determination module performs text keyword recognition on the corrected math problem text to obtain the problem attribute information corresponding to the math problem text; based on the problem attribute information, it selects a matching problem-solving model, including:

[0036] Extract all mathematical symbols present in the corrected mathematical problem text, and obtain the problem attribute information corresponding to the mathematical problem text based on the mathematical symbols with the highest frequency of occurrence.

[0037] Based on the question attribute information, a matching problem-solving model is selected from the problem-solving model library.

[0038] Furthermore, the math problem-solving execution module inputs the corrected math problem text into the problem-solving model to obtain the corresponding solution results, including:

[0039] After optimizing and training the problem-solving model, the corrected mathematical problem text is input into the problem-solving model to obtain the corresponding problem-solving results.

[0040] Compared to existing technologies, this mathematical problem-solving method and system, which combines visual scanning recognition and correction, uses a first target object image obtained from an initial scan to obtain information on the presence of characters in the image. This information is then used to adjust the second scan of the target object. The text of the second target object image obtained from the second scan is converted into mathematical problem text. This mathematical problem text undergoes correction processing and text keyword recognition to determine its problem attribute information, thereby selecting a matching problem-solving model. The problem-solving model then processes the mathematical problem text to obtain a solution. Using the first target object image obtained from the initial scan as a reference, the state of the second scan is adjusted to obtain a clear image of the target object without any content defects. Finally, the mathematical problem text is extracted from the second scan image, and text correction and text keyword recognition are performed to ensure the correctness of the mathematical problem text and the matching of the selected problem-solving model. This reduces the interference of the mathematical problem text's own content defects on the problem-solving model, improving the accuracy and reliability of the problem-solving model in solving mathematical problem texts.

[0041] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0042] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0043] 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, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating the mathematical problem-solving method combining visual scanning recognition and correction provided by the present invention.

[0045] Figure 2 This is a schematic diagram of the structure of the mathematical problem-solving system that combines visual scanning recognition and correction provided by the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] See Figure 1 This is a flowchart illustrating a mathematical problem-solving method combining visual scanning recognition and correction provided in an embodiment of the present invention. The method includes the following steps:

[0048] Step S1: After the initial scan and capture of the target object, the obtained first target object image is processed for recognition to obtain the image character presence status information; based on the image character presence status information, the capture status for the second scan and capture of the target object is adjusted.

[0049] Step S2: Perform text conversion processing on the second target object image obtained by the second scan to generate the corresponding math problem text; perform inspection processing on the math problem text, and perform correction processing on the math problem text based on the inspection processing result;

[0050] Step S3: Perform text keyword recognition on the corrected math problem text to obtain the problem attribute information corresponding to the math problem text; select the matching problem-solving model based on the problem attribute information;

[0051] Step S4: Input the corrected math problem text into the problem-solving model to obtain the corresponding solution.

[0052] The beneficial effects of the above technical solution are as follows: This mathematical problem-solving method combining visual scanning recognition and correction uses the first target object image obtained from the initial scanning and capture of the target object as a basis to obtain the character existence status information in the image, thereby adjusting the second scanning and capture of the target object; the text of the second target object image obtained from the second scanning and capture is converted into mathematical problem text, and the mathematical problem text is corrected and text keyword recognition is performed to determine the problem attribute information of the mathematical problem text, thereby selecting a matching problem-solving model; then the problem-solving model is used to process the mathematical problem text to obtain the solution result, which uses the first target object image obtained from the initial scanning and capture as a reference to adjust the state of the second scanning and capture, thereby obtaining a clear image of the target object without image content defects; then the mathematical problem text is extracted from the image obtained from the second scanning and capture, and text correction and text keyword recognition are performed to ensure the correctness of the mathematical problem text and the matching of the selected problem-solving model, reduce the interference of the content defects of the mathematical problem text itself on the problem-solving model, and improve the accuracy and reliability of the problem-solving model in solving mathematical problem texts.

[0053] Preferably, in step S1, after the initial scanning and capturing of the target object, the obtained first target object image is processed for recognition to obtain the image character presence status information; based on the image character presence status information, the capturing state for the second scanning and capturing of the target object is adjusted, including:

[0054] The target object is scanned and captured in line-by-line mode to obtain a first target object image containing the visual content of each line region of the target object; each line region on the first target object image is identified and processed to obtain the character outline feature information of each line region, which is used as the character existence status information of the image.

[0055] Based on the character outline feature information, determine whether the characters in the corresponding row area of ​​the first target object image have blurred or overlapping outlines; if so, determine the position information of the corresponding row area on the target object.

[0056] During the secondary scanning and shooting of the target object, the focus state for shooting the visual content of the corresponding row area of ​​the target object is adjusted according to the location information.

[0057] The beneficial effects of the above technical solution are as follows: In practical applications, it can photograph target objects such as paper documents or monitor displays containing text content including mathematical problems. By photographing the target object, an image containing the text content of the mathematical problems can be obtained. Furthermore, both the initial and secondary scanning of the target object are performed using a line-by-line scanning method. That is, the target object, which contains text content, is scanned line by line, and then the images captured for each line are stitched together to obtain a complete first and second target object image. For each row of the first target image, the character outline features are analyzed. These features include the shape of the characters and the relative positions of the outlines of different characters. If the outline shape of a character in a row is missing, it indicates that the character outline is blurred. If the outlines of different characters overlap, it indicates that the character outlines are overlapping. In this case, the row positions of the row areas with blurred or overlapping outlines are sequentially numbered on the target object. This information is used to adjust the focus of the corresponding row areas during subsequent secondary scanning, ensuring that the corresponding row areas are captured clearly during the secondary scanning. Furthermore, for row areas without blurred or overlapping outlines, the shooting state of the initial scan is maintained during the secondary scanning.

[0058] Preferably, in step S2, the second target object image obtained by the secondary scanning is subjected to text conversion processing to generate mathematical problem text corresponding to the second target object image; the mathematical problem text is checked, and based on the result of the check, the mathematical problem text is corrected, including:

[0059] After performing depth-of-field consistency processing on all row areas of the second target object image obtained by the second scan, the second target object image is subjected to text recognition and conversion processing to generate the text content contained in the global image of the second target object.

[0060] Semantic recognition processing is performed on the text content to obtain the mathematical problem text corresponding to the second target object image;

[0061] The mathematical problem text is processed for grammar and spelling errors, thereby correcting the grammar and spelling errors.

[0062] The beneficial effects of the above technical solution are as follows: By performing depth-of-field consistency processing on all row areas of the second target object image obtained through secondary scanning, the normality of the second target object image can be guaranteed, and image distortion can be avoided. Furthermore, text recognition and semantic recognition processing are performed on the second target object image to obtain the mathematical problem text contained in the image, and further grammatical and spelling corrections are performed to ensure the correctness of the mathematical problem text.

[0063] Preferably, in step S3, the corrected math problem text undergoes text keyword recognition to obtain the problem attribute information corresponding to the math problem text; based on the problem attribute information, a matching problem-solving model is selected, including:

[0064] Extract all mathematical symbols present in the corrected mathematical problem text, and obtain the problem attribute information corresponding to the mathematical problem text based on the mathematical symbols with the highest frequency of occurrence.

[0065] Based on the problem's attribute information, select a matching problem-solving model from the problem-solving model library.

[0066] The beneficial effects of the above technical solution are as follows: All mathematical symbols present in the corrected mathematical problem text are extracted, along with the frequency of each symbol (i.e., the average number of times each symbol appears in each line area). The problem attribute information (i.e., the mathematical domain type involved in the mathematical problem text, such as linear algebra, calculus, geometry, or probability) is obtained from the top few mathematical symbols with the highest frequency. Then, based on the problem attribute information, a matching problem-solving model is selected from the problem-solving model library. This ensures the selected problem-solving model's suitability for the mathematical problem. This problem-solving model can be, but is not limited to, a deep learning model.

[0067] Preferably, in step S4, the corrected mathematical problem text is input into the problem-solving model to obtain the corresponding solution results, including:

[0068] After optimizing and training the problem-solving model, the corrected mathematical problem text is input into the model to obtain the corresponding solution.

[0069] The beneficial effects of the above technical solution are as follows: by optimizing and training the problem-solving model in the above manner, the text of the corrected mathematical problem is input into the problem-solving model, thus maximizing the accuracy of the problem-solving model in solving mathematical problems.

[0070] See Figure 2This is a schematic diagram of the structure of a mathematical problem-solving system combining visual scanning recognition and correction provided in an embodiment of the present invention. The mathematical problem-solving system combining visual scanning recognition and correction includes:

[0071] The image capture and analysis module is used to perform initial scanning and capture of the target object, identify and process the obtained first target object image, and obtain the image character presence status information of the first target object image; based on the image character presence status information, adjust the capture status for the second scanning and capture of the target object.

[0072] The math problem text generation module is used to perform text conversion processing on the second target object image obtained by the second scan, and generate the math problem text corresponding to the second target object image; to check the math problem text, and to correct the math problem text based on the results of the check;

[0073] The math problem-solving model determination module is used to perform text keyword recognition on the corrected math problem text to obtain the problem attribute information corresponding to the math problem text; and select the matching problem-solving model based on the problem attribute information.

[0074] The math problem-solving execution module is used to input the corrected math problem text into the problem-solving model and obtain the corresponding solution results.

[0075] The beneficial effects of the above technical solution are as follows: This mathematical problem-solving system combining visual scanning recognition and correction uses the first target object image obtained from the initial scanning and capture of the target object as a basis to obtain the character existence status information in the image, thereby adjusting the second scanning and capture of the target object; the text of the second target object image obtained from the second scanning and capture is converted into mathematical problem text, and the mathematical problem text is corrected and text keyword recognition is performed to determine the problem attribute information of the mathematical problem text, thereby selecting a matching problem-solving model; then the problem-solving model is used to process the mathematical problem text to obtain the solution result, which uses the first target object image obtained from the initial scanning and capture as a reference to adjust the state of the second scanning and capture, thereby obtaining a clear image of the target object without image content defects; then the mathematical problem text is extracted from the image obtained from the second scanning and capture, and text correction and text keyword recognition are performed to ensure the correctness of the mathematical problem text and the matching of the selected problem-solving model, reduce the interference of the content defects of the mathematical problem text itself on the problem-solving model, and improve the accuracy and reliability of the problem-solving model in solving mathematical problem texts.

[0076] Preferably, after the image capturing and analysis module performs an initial scan and capture of the target object, it performs recognition processing on the obtained first target object image to obtain the image character presence status information; based on the image character presence status information, it adjusts the capturing state for a second scan and capture of the target object, including:

[0077] The target object is scanned and captured in line-by-line mode to obtain a first target object image containing the visual content of each line region of the target object; each line region on the first target object image is identified and processed to obtain the character outline feature information of each line region, which is used as the character existence status information of the image.

[0078] Based on the character outline feature information, determine whether the characters in the corresponding row area of ​​the first target object image have blurred or overlapping outlines; if so, determine the position information of the corresponding row area on the target object.

[0079] During the secondary scanning and shooting of the target object, the focus state for shooting the visual content of the corresponding row area of ​​the target object is adjusted according to the location information.

[0080] The beneficial effects of the above technical solution are as follows: In practical applications, it can photograph target objects such as paper documents or monitor displays containing text content including mathematical problems. By photographing the target object, an image containing the text content of the mathematical problems can be obtained. Furthermore, both the initial and secondary scanning of the target object are performed using a line-by-line scanning method. That is, the target object, which contains text content, is scanned line by line, and then the images captured for each line are stitched together to obtain a complete first and second target object image. For each row of the first target image, the character outline features are analyzed. These features include the shape of the characters and the relative positions of the outlines of different characters. If the outline shape of a character in a row is missing, it indicates that the character outline is blurred. If the outlines of different characters overlap, it indicates that the character outlines are overlapping. In this case, the row positions of the row areas with blurred or overlapping outlines are sequentially numbered on the target object. This information is used to adjust the focus of the corresponding row areas during subsequent secondary scanning, ensuring that the corresponding row areas are captured clearly during the secondary scanning. Furthermore, for row areas without blurred or overlapping outlines, the shooting state of the initial scan is maintained during the secondary scanning.

[0081] Preferably, the math problem text generation module performs text conversion processing on the second target object image obtained by the secondary scanning to generate math problem text corresponding to the second target object image; it then performs inspection processing on the math problem text, and based on the result of the inspection processing, it performs correction processing on the math problem text, including:

[0082] After performing depth-of-field consistency processing on all row areas of the second target object image obtained by the second scan, the second target object image is subjected to text recognition and conversion processing to generate the text content contained in the global image of the second target object.

[0083] Semantic recognition processing is performed on the text content to obtain the mathematical problem text corresponding to the second target object image;

[0084] The mathematical problem text is processed for grammar and spelling errors, thereby correcting the grammar and spelling errors.

[0085] The beneficial effects of the above technical solution are as follows: By performing depth-of-field consistency processing on all row areas of the second target object image obtained through secondary scanning, the normality of the second target object image can be guaranteed, and image distortion can be avoided. Furthermore, text recognition and semantic recognition processing are performed on the second target object image to obtain the mathematical problem text contained in the image, and further grammatical and spelling corrections are performed to ensure the correctness of the mathematical problem text.

[0086] Preferably, the math problem-solving model determination module performs text keyword recognition on the corrected math problem text to obtain the problem attribute information corresponding to the math problem text; based on the problem attribute information, it selects a matching problem-solving model, including:

[0087] Extract all mathematical symbols present in the corrected mathematical problem text, and obtain the problem attribute information corresponding to the mathematical problem text based on the mathematical symbols with the highest frequency of occurrence.

[0088] Based on the problem's attribute information, select a matching problem-solving model from the problem-solving model library.

[0089] The beneficial effects of the above technical solution are as follows: All mathematical symbols present in the corrected mathematical problem text are extracted, along with the frequency of each symbol (i.e., the average number of times each symbol appears in each line area). The problem attribute information (i.e., the mathematical domain type involved in the mathematical problem text, such as linear algebra, calculus, geometry, or probability) is obtained from the top few mathematical symbols with the highest frequency. Then, based on the problem attribute information, a matching problem-solving model is selected from the problem-solving model library. This ensures the selected problem-solving model's suitability for the mathematical problem. This problem-solving model can be, but is not limited to, a deep learning model.

[0090] Preferably, the math problem-solving execution module inputs the corrected math problem text into the problem-solving model to obtain the corresponding solution results, including:

[0091] After optimizing and training the problem-solving model, the corrected mathematical problem text is input into the model to obtain the corresponding solution.

[0092] The beneficial effects of the above technical solution are as follows: by optimizing and training the problem-solving model in the above manner, the text of the corrected mathematical problem is input into the problem-solving model, thus maximizing the accuracy of the problem-solving model in solving mathematical problems.

[0093] As can be seen from the above embodiments, this mathematical problem-solving method and system combining visual scanning recognition and correction uses a first target object image obtained from an initial scan as a basis to obtain the character existence status information in the image, thereby adjusting the second scan of the target object. The text of the second target object image obtained from the second scan is converted into mathematical problem text, and the mathematical problem text undergoes correction processing and text keyword recognition to determine the problem attribute information, thereby selecting a matching problem-solving model. The problem-solving model then processes the mathematical problem text to obtain a solution result. Using the first target object image obtained from the initial scan as a reference, the state of the second scan is adjusted to obtain a clear image of the target object without any image content defects. The mathematical problem text is then extracted from the image obtained from the second scan, and text correction and text keyword recognition are performed to ensure the correctness of the mathematical problem text and the matching of the selected problem-solving model, reducing the interference of the mathematical problem text's own content defects on the problem-solving model, and improving the accuracy and reliability of the problem-solving model in solving mathematical problem texts.

[0094] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A mathematical problem-solving method combining visual scanning recognition and correction, characterized in that, Includes the following steps: Step S1: After the initial scan and capture of the target object, the obtained first target object image is processed for recognition to obtain the character presence status information of the first target object image; based on the character presence status information, the capture state for the second scan and capture of the target object is adjusted, including: scanning and capturing the target object in line-by-line mode to obtain a first target object image containing the visual content of each line area of ​​the target object; processing each line area on the first target object image to obtain the character outline feature information of each line area, which is used as the character presence status information; based on the character outline feature information, determining whether the characters in the corresponding line area of ​​the first target object image have blurred or overlapping outlines; if so, determining the position information of the corresponding line area on the target object; during the second scan and capture of the target object, the focus state for capturing the visual content of the corresponding line area of ​​the target object is adjusted based on the position information; Step S2: Perform text conversion processing on the second target object image obtained by the secondary scanning to generate the math problem text corresponding to the second target object image; perform inspection processing on the math problem text, and perform correction processing on the math problem text based on the inspection processing result; Step S3: Perform text keyword recognition on the corrected math problem text to obtain the problem attribute information corresponding to the math problem text; select a matching problem-solving model based on the problem attribute information; Step S4: Input the corrected math problem text into the problem-solving model to obtain the corresponding solution.

2. The mathematical problem-solving method combining visual scanning recognition and correction as described in claim 1, characterized in that: In step S2, the second target object image obtained by the secondary scanning is subjected to text conversion processing to generate the mathematical problem text corresponding to the second target object image; The mathematical problem text is inspected and processed, and based on the results of the inspection and processing, the mathematical problem text is corrected, including: After performing depth-of-field consistency processing on all row areas of the second target object image obtained by the secondary scanning, the second target object image is subjected to text recognition and conversion processing to generate the text content contained in the global image of the second target object. The text content is subjected to semantic recognition processing to obtain the math problem text corresponding to the second target object image; The mathematical problem text is subjected to grammar and spelling detection processing, thereby correcting the grammar and spelling errors in the mathematical problem text.

3. The mathematical problem-solving method combining visual scanning recognition and correction as described in claim 2, characterized in that: In step S3, the corrected math problem text undergoes text keyword recognition to obtain the problem attribute information corresponding to the math problem text; based on the problem attribute information, a matching problem-solving model is selected, including: Extract all mathematical symbols present in the corrected mathematical problem text, and obtain the problem attribute information corresponding to the mathematical problem text based on the mathematical symbols with the highest frequency of occurrence. Based on the question attribute information, a matching problem-solving model is selected from the problem-solving model library.

4. The mathematical problem-solving method combining visual scanning recognition and correction as described in claim 3, characterized in that: In step S4, the corrected mathematical problem text is input into the problem-solving model to obtain the corresponding solution results, including: After optimizing and training the problem-solving model, the corrected mathematical problem text is input into the problem-solving model to obtain the corresponding problem-solving results.

5. A mathematical problem-solving system combining visual scanning recognition and correction, characterized in that: include: The image capture and analysis module is used to perform initial scanning and capture of the target object, identify and process the obtained first target object image, and obtain the image character presence status information of the first target object image; and adjust the capture status of the second scanning and capture of the target object based on the image character presence status information. The math problem text generation module is used to perform text conversion processing on the second target object image obtained by the secondary scanning and generate math problem text corresponding to the second target object image. The text of the mathematical problem is inspected and processed, and the text of the mathematical problem is corrected based on the results of the inspection and processing. The math problem-solving model determination module is used to perform text keyword recognition on the corrected math problem text to obtain the problem attribute information corresponding to the math problem text; and select a matching problem-solving model based on the problem attribute information. The math problem-solving execution module is used to input the corrected math problem text into the problem-solving model and obtain the corresponding solution result; The image capture and analysis module is also used for: The target object is scanned and photographed in line-by-line mode to obtain a first target object image containing the visual content of each line region of the target object; each line region on the first target object image is identified and processed to obtain the character outline feature information of each line region, which is used as the character existence status information of the image. Based on the character outline feature information, determine whether the characters in the corresponding row area of ​​the first target object image have blurred or overlapping outlines; if so, determine the position information of the corresponding row area on the target object. During the secondary scanning and shooting of the target object, the focus state for shooting the visual content of the corresponding row area of ​​the target object is adjusted according to the position information.

6. The mathematical problem-solving system combining visual scanning recognition and correction as described in claim 5, characterized in that: The math problem text generation module performs text conversion processing on the second target object image obtained by the secondary scanning to generate math problem text corresponding to the second target object image. The mathematical problem text is inspected and processed, and based on the results of the inspection and processing, the mathematical problem text is corrected, including: After performing depth-of-field consistency processing on all row areas of the second target object image obtained by the secondary scanning, the second target object image is subjected to text recognition and conversion processing to generate the text content contained in the global image of the second target object. The text content is subjected to semantic recognition processing to obtain the math problem text corresponding to the second target object image; The mathematical problem text is subjected to grammar and spelling detection processing, thereby correcting the grammar and spelling errors in the mathematical problem text.

7. The mathematical problem-solving system combining visual scanning recognition and correction as described in claim 6, characterized in that: The math problem-solving model determination module performs text keyword recognition on the corrected math problem text to obtain the problem attribute information corresponding to the math problem text; based on the problem attribute information, it selects a matching problem-solving model, including: Extract all mathematical symbols present in the corrected mathematical problem text, and obtain the problem attribute information corresponding to the mathematical problem text based on the mathematical symbols with the highest frequency of occurrence. Based on the question attribute information, a matching problem-solving model is selected from the problem-solving model library.

8. The mathematical problem-solving system combining visual scanning recognition and correction as described in claim 7, characterized in that: The math problem-solving execution module inputs the corrected math problem text into the problem-solving model to obtain the corresponding solution results, including: After optimizing and training the problem-solving model, the corrected mathematical problem text is input into the problem-solving model to obtain the corresponding problem-solving results.

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