Selection question positioning processing system and method based on computer vision

Abstract

The invention discloses a choice question positioning processing system and method based on computer vision, and the method comprises the following steps: cutting a picture of a choice question from a test paper picture according to the size and style of a test paper, carrying out the graying, binarization and noise removal processing of a test paper scanning image, and obtaining a preprocessed image; on the preprocessed image, extracting three features related to a choice question filling area, including actual filling of students, a choice question filling area square frame and choice question filling area square frame line segments; based on the extracted feature information, starting point coordinates of a filling area of the choice questions are calculated, and the starting point of the filling area is the upper left corner position of the first choice item; boundary adjustment, interference elimination and image correction processing are carried out on the full-filling area obtained through calculation, a final full-filling area positioning result is obtained, and the full-filling area positioning method has the advantages of being high in automation degree and accurate in positioning.

Description

Technical Field

[0001] The present invention relates to the technical field of computer vision, and in particular to a multiple-choice question positioning processing system and method based on computer vision. Background Art

[0002] Traditional examination marking mainly relies on manual work, which is not only time-consuming and labor-intensive, but also has a certain degree of subjectivity. With the rapid development of computer vision technology, more and more application scenarios begin to involve automated examination marking, especially the automatic marking of objective questions such as multiple-choice questions and fill-in-the-blank questions. However, in actual applications, there are often some variables in the physical state of the examination paper and the students' answering behavior, such as poor examination paper printing quality, tilt and deformation of scanned images, and writing interference when students answer questions. These factors bring challenges to the positioning and recognition of multiple-choice questions.

[0003] Existing technical solutions usually adopt a positioning mark method to set positioning marks on multiple-choice test papers for the alignment and calibration of multiple-choice questions in the automatic marking system. However, this technical solution has limitations when it is necessary to process test papers of various formats, especially when the test paper format, the position or size of the positioning marks change, or when there are no positioning marks, the existing automatic marking system may not work properly, resulting in reduced marking accuracy or inability to perform automatic marking. Therefore, it is necessary to design a multiple-choice question positioning processing system and method based on computer vision with high automation and accurate positioning. Summary of the invention

[0004] The purpose of the present invention is to provide a system and method for locating and processing multiple-choice questions based on computer vision to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solution: a method for locating and processing multiple-choice questions based on computer vision, comprising the following steps:

[0006] A. According to the size and style of the test paper, cut out the picture of the multiple-choice questions from the test paper picture, grayscale, binarize and remove noise from the scanned image of the test paper to obtain the pre-processed image;

[0007] B. Extract three features related to the filling area of ​​the multiple-choice questions from the pre-processed image, including: the actual filling of the students, the box of the filling area of ​​the multiple-choice questions, and the line segments of the box of the filling area of ​​the multiple-choice questions;

[0008] C. Based on the extracted feature information, calculate the starting point coordinates of the filling area of ​​the multiple-choice question;

[0009] D. Perform boundary adjustment, interference elimination and image correction on the calculated filling area to obtain the final filling area positioning result.

[0010] According to the above technical solution, step B specifically includes:

[0011] Students' actual filling: Use the OTSU algorithm to convert the grayscale image into a binary image, use the morphological opening operation to eliminate the filling frame and the ABCD letters of the multiple-choice questions other than the black filled items, then use the contour search algorithm to find the contour of the filled area, match the search contour length greater than the preset value to find the filled area, and finally calculate the x, y coordinates of the first multiple-choice question based on the known filling. If there is no suitable filling, the calculated x or y value is invalid;

[0012] Multiple-choice question filling area box: Use the adaptive threshold algorithm to convert the grayscale image into a binary image, and then use the contour search algorithm to find the contour of the filling area box. According to the matching search, the part with a contour length greater than the preset value is the search for the filling area. Finally, based on the known filling area, calculate the x, y coordinates of the first multiple-choice question; if there is no suitable filling, the calculated x or y value is invalid.

[0013] Line segments of the filling area box of multiple-choice questions: Through morphological operations, the line segment features in the image are extracted, including horizontal line segments and vertical line segments. Then, the line segment detection algorithm is used to find the line segments of the filling area box. According to the position and length information of the line segments, the position and range of the filling area box are determined, and the x, y coordinates of the first multiple-choice question are calculated; if there is no suitable filling, the calculated x or y value is invalid.

[0014] According to the above technical solution, the calculation of the starting point coordinates of the filling area of ​​the multiple-choice question in step C specifically includes:

[0015] For each multiple-choice question starting point coordinate obtained by each feature extraction method, a credibility value is attached.

[0016] Among the starting point coordinates obtained by the three feature extraction methods, the coordinates with the highest credibility are selected as the final starting point coordinates of the filling area to achieve comprehensive value selection.

[0017] According to the above technical solution, the credibility value calculation method includes:

[0018] Step S1: Collect and preprocess data, including cleaning data, removing duplicate values, and processing missing values;

[0019] Step S2: According to the definition and quantification method of the evaluation indicators, score each indicator to obtain S1, S2, and S3;

[0020] Step S3: According to the importance of the evaluation indicators, weights W1, W2, and W3 are assigned, where W1+W2+W3=1;

[0021] Step S4: Use the credibility calculation formula, substitute the quantized value and the weight into the formula, and calculate the credibility value C;

[0022] Step S5: Output the credibility value C.

[0023] According to the above technical solution, the comprehensive value obtaining method includes:

[0024] Step A1: Set the starting point coordinates obtained by the student's actual filling to (x1, y1), and the accompanying credibility output value to C1;

[0025] Set the starting point coordinates of the multiple choice question filling area box to (x2, y2), and the accompanying credibility output value to C2;

[0026] Set the starting point coordinates of the box segment of the multiple-choice question filling area to (x3, y3), and the accompanying credibility output value to C3;

[0027] Set the credibility threshold T. When the credibility C ≥ T, the corresponding coordinates are considered valid coordinates.

[0028] Step A2: Filter valid coordinates according to the credibility threshold T: when C1 ≥ T, (x1, y1) is a valid coordinate, when C2 ≥ T, (x2, y2) is a valid coordinate, when C3 ≥ T, (x3, y3) is a valid coordinate, and output the filtered valid coordinates (x i ,y i ), where i is a value of 1, 2, or 3, capturing the number of valid coordinates;

[0029] Step A3: When the number of captured valid coordinates is 1, the coordinate is directly selected as the final starting coordinate of the filling area;

[0030] When the number of captured valid coordinates is greater than 1, the credibility value is compared and the final coordinate (x final ,y final ), where (x final ,y final )=argC i , where (x i ,y i )∈valid coordinates, that is, the coordinate with the highest credibility is selected as the final starting coordinate of the filling area;

[0031] When the number of captured valid coordinates is 0, the multiple-choice question positioning processing step of the current test paper is terminated, and a prompt signal is output to remind the staff to perform manual positioning.

[0032] According to the above technical solution, in step S4, the credibility calculation formula is:

[0033] C=W1*S1+W2*S2+W3*S3

[0034] In the formula, C represents the credibility value, W1, W2, and W3 represent the weights of the credibility of the information source, the credibility of the information content, and the relevance of the target, respectively; S1, S2, and S3 represent the quantitative values ​​of the credibility of the information source, the credibility of the information content, and the relevance evaluation index of the target, respectively.

[0035] According to the above technical solution, the system for positioning and processing multiple-choice questions based on computer vision includes an image preprocessing module, a feature extraction module, a starting point coordinate calculation module and a post-processing module, wherein the image preprocessing module, the feature extraction module, the starting point coordinate calculation module and the post-processing module are electrically connected to each other; wherein,

[0036] The image preprocessing module is used to perform grayscale, binarization and noise removal processing on the scanned image of the test paper;

[0037] The feature extraction module is used to extract three features related to the multiple-choice question filling area on the pre-processed image, including the student's actual filling, the multiple-choice question filling area box and the multiple-choice question filling area box line segment;

[0038] The starting point coordinate calculation module is used to calculate the starting point coordinates of the filling area of ​​the multiple-choice question based on the extracted feature information;

[0039] The post-processing module is used to perform boundary adjustment, interference elimination and image correction processing on the calculated filling area and obtain the final filling area positioning result.

[0040] According to the above technical solution, the feature extraction module includes a student actual filling extraction submodule, a multiple-choice question filling area box extraction submodule, and a multiple-choice question filling area box line segment extraction submodule; wherein,

[0041] The student actual filling extraction submodule is used to extract the student's actual filling features;

[0042] The multiple choice question filling area box extraction submodule is used to extract the features of the multiple choice question filling area box;

[0043] The multiple-choice question filling area box line segment extraction submodule is used to extract the box line segment features of the multiple-choice question filling area.

[0044] According to the above technical solution, the starting point coordinate calculation module includes a credibility evaluation unit, which is used to attach a credibility value to the starting point coordinates of each multiple-choice question obtained by the feature extraction method, and the credibility evaluation unit further includes a data preprocessing subunit, a quantization subunit, a weight allocation subunit and a credibility calculation subunit, and the data preprocessing subunit, the quantization subunit, the weight allocation subunit and the credibility calculation subunit are electrically connected to each other; wherein,

[0045] The data preprocessing subunit is used to collect and preprocess data;

[0046] The quantitative scoring subunit is used to score each indicator according to the definition and quantification method of the evaluation indicator;

[0047] The weight allocation subunit is used to allocate weight values ​​according to the importance of the evaluation index;

[0048] The credibility calculation subunit is used to calculate the credibility value.

[0049] According to the above technical solution, the starting point coordinate calculation module further includes a comprehensive value taking unit, and the comprehensive value taking unit is used to:

[0050] Set the starting point coordinates obtained by the three feature extraction methods and their corresponding credibility values;

[0051] Set the credibility threshold to filter valid coordinates;

[0052] Based on the number of captured valid coordinates, the final starting coordinates of the filling area are selected.

[0053] Compared with the prior art, the beneficial effects achieved by the present invention are: the present invention, through analyzing three different features of the filling area of ​​the multiple-choice questions, does not require additional multiple-choice question positioning marks when the scanned piece is tilted, deformed, or interfered, and can accurately locate the starting point of the multiple-choice questions, thereby improving the accuracy and robustness of multiple-choice question positioning and recognition, thereby greatly improving the recognition rate of multiple-choice questions, reducing the requirements of the recognition program on the test paper format, and being able to adapt to test papers of different formats in various regions and schools. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0055] In the attached picture:

[0056] Figure 1 A flowchart of the steps of a method for locating and processing multiple-choice questions based on computer vision provided in the first embodiment of the present invention;

[0057] Figure 2 A schematic diagram of the module composition of a multiple-choice question positioning processing system based on computer vision provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] Embodiment 1

[0060] See also Figure 1 The present invention provides a technical solution: a method for positioning and processing multiple-choice questions based on computer vision, comprising the following steps:

[0061] A. According to the size and style of the test paper, cut out the picture of the multiple-choice questions from the test paper picture, grayscale, binarize and remove noise from the scanned image of the test paper to obtain the pre-processed image;

[0062] B. Extract three features related to the filling area of ​​the multiple-choice questions from the pre-processed image, including: the actual filling of the students, the box of the filling area of ​​the multiple-choice questions, and the line segments of the box of the filling area of ​​the multiple-choice questions;

[0063] C. Based on the extracted feature information, calculate the starting point coordinates of the filling area of ​​the multiple-choice question, where the starting point of the filling area is the upper left corner of the first option;

[0064] D. Perform boundary adjustment, interference elimination and image correction on the calculated filling area to obtain the final filling area positioning result.

[0065] Step B specifically includes:

[0066] Students' actual filling: Use the OTSU algorithm to convert the grayscale image into a binary image, use the morphological opening operation to eliminate the filling frame and the ABCD letters of the multiple-choice questions other than the black filled items, then use the contour search algorithm to find the contour of the filled area, match the search contour length greater than the preset value to find the filled area, and finally calculate the x, y coordinates of the first multiple-choice question based on the known filling. If there is no suitable filling, the calculated x or y value is invalid;

[0067] Multiple-choice question filling area box: Use the adaptive threshold algorithm to convert the grayscale image into a binary image, and then use the contour search algorithm to find the contour of the filling area box. According to the matching search, the part with a contour length greater than the preset value is the search for the filling area. Finally, based on the known filling area, calculate the x, y coordinates of the first multiple-choice question; if there is no suitable filling, the calculated x or y value is invalid.

[0068] Line segments of the filling area box of multiple-choice questions: extract line segment features in the image through morphological operations, including horizontal line segments and vertical line segments, and then use the line segment detection algorithm to find the line segments of the filling area box. According to the position and length information of the line segments, determine the position and range of the filling area box, and calculate the x, y coordinates of the first multiple-choice question; if there is no suitable filling, the calculated x or y value is invalid, and the line segment detection algorithm includes but is not limited to Hough transform.

[0069] The starting point coordinates of the filling area of ​​the calculation multiple-choice question in step C specifically include:

[0070] For each of the starting coordinates of the multiple-choice questions obtained by the feature extraction method, a credibility value is attached; wherein the credibility value can be obtained by comprehensively evaluating factors such as the integrity of the contour, size consistency, and positional relationship.

[0071] Among the starting point coordinates obtained by the three feature extraction methods, the coordinates with the highest credibility are selected as the final starting point coordinates of the filling area to achieve comprehensive value selection; if the coordinates obtained by a certain method are invalid, such as no suitable contour or line segment is found, its credibility is set to 0 and excluded from the final selection.

[0072] The credibility value calculation method includes:

[0073] Step S1: Collect and preprocess data, including cleaning data, removing duplicate values, and processing missing values;

[0074] Step S2: According to the definition and quantification method of the evaluation indicators, score each indicator to obtain S1, S2, and S3; the quantification range is 0 to 1, where 1 means completely credible and 0 means completely unreliable, where S1 is the score value based on the authority and historical accuracy of the information source; S2 is the score value based on the accuracy, timeliness, and completeness of the information content; S3 is the correlation between the data and the target calculated using the correlation measurement method and converted into a quantitative value from 0 to 1;

[0075] Step S3: According to the importance of the evaluation index, weights W1, W2, and W3 are assigned, where W1+W2+W3=1; when the importance of the evaluation index is higher, the corresponding weight value W is larger;

[0076] Step S4: Use the credibility calculation formula, substitute the quantized value and the weight into the formula, and calculate the credibility value C;

[0077] Step S5: Output the credibility value C; through the above steps, the credibility of the starting point coordinates of the multiple-choice questions obtained by each feature extraction method can be calculated more accurately, and the most reliable coordinates can be comprehensively selected as the final starting point coordinates of the filling area, thereby effectively improving the accuracy and robustness of the multiple-choice question positioning processing.

[0078] The comprehensive value methods include:

[0079] Step A1: Set the starting point coordinates obtained by the student's actual filling to (x1, y1), and the accompanying credibility output value to C1;

[0080] Set the starting point coordinates of the multiple choice question filling area box to (x2, y2), and the accompanying credibility output value to C2;

[0081] Set the starting point coordinates of the box segment of the multiple-choice question filling area to (x3, y3), and the accompanying credibility output value to C3;

[0082] Set the credibility threshold T. When the credibility C ≥ T, the corresponding coordinates are considered valid coordinates.

[0083] Step A2: Filter valid coordinates according to the credibility threshold T: when C1 ≥ T, (x1, y1) is a valid coordinate, when C2 ≥ T, (x2, y2) is a valid coordinate, when C3 ≥ T, (x3, y3) is a valid coordinate, and output the filtered valid coordinates (x i ,y i ), where i is a value of 1, 2, or 3, capturing the number of valid coordinates;

[0084] Step A3: When the number of captured valid coordinates is 1, the coordinate is directly selected as the final starting coordinate of the filling area;

[0085] When the number of captured valid coordinates is greater than 1, the credibility value is compared and the final coordinate (x final ,y final ), where (x final ,y final )=argC i , where (x i ,y i )∈valid coordinates, that is, the coordinate with the highest credibility is selected as the final starting coordinate of the filling area;

[0086] When the number of captured valid coordinates is 0, the multiple-choice question positioning processing step of the current test paper is terminated, and a prompt signal is output to remind the staff to perform manual positioning.

[0087] In step S4, the credibility calculation formula is:

[0088] C=W1*S1+W2*S2+W3*S3

[0089] In the formula, C represents the credibility value, W1, W2, and W3 represent the weights of the credibility of the information source, the credibility of the information content, and the relevance of the target, respectively; S1, S2, and S3 represent the quantitative values ​​of the credibility of the information source, the credibility of the information content, and the relevance evaluation index of the target, respectively.

[0090] In the above steps, by analyzing the three different features of the multiple-choice question filling area, when the scanned copy is tilted, deformed, or interfered, no additional multiple-choice question positioning marks are required, and the starting point of the multiple-choice questions can be accurately located, thereby improving the accuracy and robustness of multiple-choice question positioning and recognition, thereby greatly improving the recognition rate of multiple-choice questions, reducing the requirements of the recognition program for the test paper format, and being able to adapt to test papers of different formats in various regions and schools.

[0091] Embodiment 2

[0092] See also Figure 2 The present invention also provides a computer vision-based multiple-choice question positioning processing system for implementing the method in Example 1, the system includes an image preprocessing module, a feature extraction module, a starting point coordinate calculation module and a post-processing module, and the image preprocessing module, the feature extraction module, the starting point coordinate calculation module and the post-processing module are electrically connected to each other; wherein,

[0093] The image preprocessing module is used to perform grayscale, binarization and noise removal processing on the scanned image of the test paper;

[0094] The feature extraction module is used to extract three features related to the multiple-choice question filling area on the pre-processed image, including the student's actual filling, the multiple-choice question filling area box and the multiple-choice question filling area box line segment;

[0095] The starting point coordinate calculation module is used to calculate the starting point coordinates of the filling area of ​​the multiple-choice question based on the extracted feature information;

[0096] The post-processing module is used to perform boundary adjustment, interference elimination and image correction processing on the calculated filling area and obtain the final filling area positioning result.

[0097] The feature extraction module includes a student's actual filling extraction submodule, a multiple-choice question filling area box extraction submodule, and a multiple-choice question filling area box line segment extraction submodule;

[0098] The student actual filling extraction submodule is used to extract the student's actual filling features;

[0099] The multiple choice question filling area box extraction submodule is used to extract the features of the multiple choice question filling area box;

[0100] The multiple-choice question filling area box line segment extraction submodule is used to extract the box line segment features of the multiple-choice question filling area.

[0101] The starting point coordinate calculation module includes a credibility evaluation unit, which is used to attach a credibility value to the starting point coordinates of each multiple-choice question obtained by the feature extraction method. The credibility evaluation unit further includes a data preprocessing subunit, a quantization subunit, a weight allocation subunit and a credibility calculation subunit. The data preprocessing subunit, the quantization subunit, the weight allocation subunit and the credibility calculation subunit are electrically connected to each other; wherein,

[0102] The data preprocessing subunit is used to collect and preprocess data;

[0103] The quantitative scoring subunit is used to score each indicator according to the definition and quantification method of the evaluation indicator;

[0104] The weight allocation subunit is used to allocate weight values ​​according to the importance of the evaluation index;

[0105] The credibility calculation subunit is used to calculate the credibility value.

[0106] The starting point coordinate calculation module also includes a comprehensive value obtaining unit, which is used to:

[0107] Set the starting point coordinates obtained by the three feature extraction methods and their corresponding credibility values;

[0108] Set the credibility threshold to filter valid coordinates;

[0109] According to the number of captured valid coordinates, the final starting coordinates of the filling area are selected;

[0110] Compared with the existing positioning marking method, which has limitations when processing various formats of test papers, may not work properly, reduce the accuracy of marking, or cannot automatically mark the test papers, advanced image processing and intelligent algorithms are used to improve the accuracy and robustness of multiple-choice question positioning and recognition. In the face of the above challenges, the system can still accurately identify the answers to multiple-choice questions, thereby improving the automation and accuracy of test evaluation.

[0111] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0112] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for positioning and processing multiple-choice questions based on computer vision, characterized in that: The following steps are involved: A. According to the size and style of the test paper, cut out the picture of the multiple-choice questions from the test paper picture, grayscale, binarize and remove noise from the scanned image of the test paper to obtain the pre-processed image; B. Extract three features related to the filling area of ​​the multiple-choice questions from the pre-processed image, including: the actual filling of the students, the box of the filling area of ​​the multiple-choice questions, and the line segments of the box of the filling area of ​​the multiple-choice questions; C. Based on the extracted feature information, calculate the starting point coordinates of the filling area of ​​the multiple-choice question; D. Perform boundary adjustment, interference elimination and image correction on the calculated filling area to obtain the final filling area positioning result.

2. The method for locating and processing multiple-choice questions based on computer vision according to claim 1, characterized in that: Step B specifically includes: Students' actual filling: Use the OTSU algorithm to convert the grayscale image into a binary image, use the morphological opening operation to eliminate the filling frame and the ABCD letters of the multiple-choice questions other than the black filled items, then use the contour search algorithm to find the contour of the filled area, match the search contour length greater than the preset value to find the filled area, and finally calculate the x, y coordinates of the first multiple-choice question based on the known filling. If there is no suitable filling, the calculated x or y value is invalid; Multiple-choice question filling area box: Use the adaptive threshold algorithm to convert the grayscale image into a binary image, then use the contour search algorithm to find the contour of the filling area box, and search for the part with a contour length greater than the preset value based on the matching search to find the filling area. Finally, based on the known filling area, calculate the x, y coordinates of the first multiple-choice question; if there is no suitable filling, the calculated x or y value is invalid; Line segments of the filling area box of multiple-choice questions: Through morphological operations, the line segment features in the image are extracted, including horizontal line segments and vertical line segments. Then, the line segment detection algorithm is used to find the line segments of the filling area box. According to the position and length information of the line segments, the position and range of the filling area box are determined, and the x, y coordinates of the first multiple-choice question are calculated; if there is no suitable filling, the calculated x or y value is invalid.

3. The method for locating and processing multiple-choice questions based on computer vision according to claim 1, characterized in that: The calculation of the starting point coordinates of the filling area of ​​the multiple-choice question in step C specifically includes: For each multiple-choice question starting point coordinate obtained by each feature extraction method, a credibility value is attached; Among the starting point coordinates obtained by the three feature extraction methods, the coordinates with the highest credibility are selected as the final starting point coordinates of the filling area to achieve comprehensive value selection.

4. The method for locating and processing multiple-choice questions based on computer vision according to claim 3, characterized in that: The credibility value calculation method includes: Step S1: Collect and preprocess data, including cleaning data, removing duplicate values, and processing missing values; Step S2: According to the definition and quantification method of the evaluation indicators, score each indicator to obtain S1, S2, and S3; Step S3: According to the importance of the evaluation indicators, weights W1, W2, and W3 are assigned, where W1+W2+W3=1; Step S4: Use the credibility calculation formula, substitute the quantized value and the weight into the formula, and calculate the credibility value C; Step S5: Output the credibility value C.

5. The method for locating and processing multiple-choice questions based on computer vision according to claim 3, characterized in that: The comprehensive value methods include: Step A1: Set the starting point coordinates obtained by the student's actual filling to (x1, y1), and the accompanying credibility output value to C1; Set the starting point coordinates of the multiple choice question filling area box to (x2, y2), and the accompanying credibility output value to C2; Set the starting point coordinates of the box segment of the multiple-choice question filling area to (x3, y3), and the accompanying credibility output value to C3; Set the credibility threshold T. When the credibility C ≥ T, the corresponding coordinates are considered valid coordinates. Step A2: Filter valid coordinates according to the credibility threshold T: when C1 ≥ T, (x1, y1) is a valid coordinate, when C2 ≥ T, (x2, y2) is a valid coordinate, when C3 ≥ T, (x3, y3) is a valid coordinate, and output the filtered valid coordinates (x i ,y i ), where i is a value of 1, 2, or 3, capturing the number of valid coordinates; Step A3: When the number of captured valid coordinates is 1, the coordinate is directly selected as the final starting coordinate of the filling area; When the number of captured valid coordinates is greater than 1, the credibility value is compared and the final coordinate (x final ,y final ), where (x final ,y final )=argC i , where (x i ,y i )∈valid coordinates, that is, the coordinate with the highest credibility is selected as the final starting coordinate of the filling area; When the number of captured valid coordinates is 0, the multiple-choice question positioning processing step of the current test paper is terminated, and a prompt signal is output to remind the staff to perform manual positioning.

6. The method for locating and processing multiple-choice questions based on computer vision according to claim 4, characterized in that: In step S4, the credibility calculation formula is: C=W1*S1+W2*S2+W3*S3 In the formula, C represents the credibility value, W1, W2, and W3 represent the weights of the credibility of the information source, the credibility of the information content, and the relevance of the target, respectively; S1, S2, and S3 represent the quantitative values ​​of the credibility of the information source, the credibility of the information content, and the relevance evaluation index of the target, respectively.

7. A multiple-choice question positioning and processing system based on computer vision, characterized in that: The system includes an image preprocessing module, a feature extraction module, a starting point coordinate calculation module and a post-processing module, wherein the image preprocessing module, the feature extraction module, the starting point coordinate calculation module and the post-processing module are electrically connected to each other; wherein, The image preprocessing module is used to perform grayscale, binarization and noise removal processing on the scanned image of the test paper; The feature extraction module is used to extract three features related to the multiple-choice question filling area on the pre-processed image, including the student's actual filling, the multiple-choice question filling area box and the multiple-choice question filling area box line segment; The starting point coordinate calculation module is used to calculate the starting point coordinates of the filling area of ​​the multiple-choice question based on the extracted feature information; The post-processing module is used to perform boundary adjustment, interference elimination and image correction processing on the calculated filling area and obtain the final filling area positioning result.

8. The computer vision-based multiple-choice question positioning processing system according to claim 7, characterized in that: The feature extraction module includes a student actual filling extraction submodule, a multiple-choice question filling area box extraction submodule, and a multiple-choice question filling area box line segment extraction submodule; wherein, The student actual filling extraction submodule is used to extract the student's actual filling features; The multiple choice question filling area box extraction submodule is used to extract the features of the multiple choice question filling area box; The multiple-choice question filling area box line segment extraction submodule is used to extract the box line segment features of the multiple-choice question filling area.

9. The multiple choice question positioning processing system based on computer vision according to claim 7, characterized in that: The starting point coordinate calculation module includes a credibility evaluation unit, which is used to attach a credibility value to the starting point coordinates of each multiple-choice question obtained by the feature extraction method. The credibility evaluation unit further includes a data preprocessing subunit, a quantization subunit, a weight allocation subunit and a credibility calculation subunit. The data preprocessing subunit, the quantization subunit, the weight allocation subunit and the credibility calculation subunit are electrically connected to each other; wherein, The data preprocessing subunit is used to collect and preprocess data; The quantitative scoring subunit is used to score each indicator according to the definition and quantification method of the evaluation indicator; The weight allocation subunit is used to allocate weight values ​​according to the importance of the evaluation index; The credibility calculation subunit is used to calculate the credibility value.

10. The multiple choice question positioning processing system based on computer vision according to claim 9, characterized in that: The starting point coordinate calculation module further includes a comprehensive value obtaining unit, which is used to: Set the starting point coordinates obtained by the three feature extraction methods and their corresponding credibility values; Set the credibility threshold to filter valid coordinates; Based on the number of captured valid coordinates, the final starting coordinates of the filling area are selected.

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