CN110659584A - Intelligent trace marking system based on image recognition - Google Patents
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Abstract
The invention provides an intelligent marking scoring system based on image recognition, which comprises: the customized answer sheet module is used for generating answer sheet information; the image processing module is used for scanning the two-dimensional code to obtain the test paper number, obtaining a corresponding coordinate information description table and test paper test question information according to the test paper number, and comparing the actual coordinate position of the mark point of the scanned image with the mark point coordinate position recorded in the positioning information table; the data processing module is used for obtaining the identification result tables of all reference students, further obtaining answer information of the test questions, respectively calculating scores of the objective questions and the subjective questions, obtaining the scores and the ranks of the single test paper of each student, and storing the scores and the ranks into the calculation result tables. The invention can quickly know the knowledge mastering condition of the students through the report forms, is convenient for performing targeted explanation, simplifies the examination paper marking operation process and greatly reduces the learning cost of teachers.
Description
Technical Field
The invention relates to the technical field of computers and image recognition, in particular to an intelligent marking scoring system based on image recognition.
Background
With the development of computer technology, the traditional marking mode adopting an artificial objective question is replaced by an intelligent marking mode, so that the marking accuracy and marking efficiency are greatly improved.
However, in the existing automatic paper marking systems, reading of a study number and identification of objective questions are mostly completed by a computer, and then a teacher intensively reviews subjective questions, training of the teacher needs to be performed in advance, so that human resources are consumed greatly, manpower is wasted in dealing with small-sized examinations of test types, and collection of wrong test questions for each student and intensive exercises of similar questions cannot be provided. In the scene of rapid examination, the traditional paper marking system needs to train teachers, and the traditional paper marking system is inconvenient to use because of centralized paper marking.
Disclosure of Invention
The object of the present invention is to solve at least one of the technical drawbacks mentioned.
Therefore, the invention aims to provide an intelligent marking scoring system based on image recognition.
In order to achieve the above object, an embodiment of the present invention provides an intelligent marking scoring system based on image recognition, including: a customized answer sheet module, an image processing module and a data processing module, wherein,
the customized answer sheet module is used for generating answer sheet information, and comprises: the examination paper number, the two-dimensional code corresponding to the examination paper number, the positioning mark, the scoring frame, the basic information of an examinee, the input label of a total column, the objective question number, the filling area and the positioning information table for describing the information of the whole answer sheet;
the image processing module is used for scanning the two-dimensional code to obtain the test paper number, obtaining a corresponding coordinate information description table and test paper question information according to the test paper number, comparing the actual coordinate position of the mark point of the scanned image with the mark point coordinate position recorded in the positioning information table, calculating the scale between the test paper scanned image and the test paper layout generated by typesetting according to the actual coordinate position, converting and correcting to the size designed previously; then, according to a coordinate information table generated by an answer sheet customizing module, cutting an image area corresponding to each test question into a privacy area, an objective question area, a subjective question area and a handwriting scoring frame area; for the objective question area, identifying the result filled by the student according to the number of options and the coordinate information, drawing the identification result on an identification result image, and storing the identification result in an identification result table; for the subjective question area, calling a previously trained recognition model to recognize the handwritten image of the teacher in the scoring frame, drawing a recognition result on the recognition result image, and storing the recognition result image in a recognition result table; sending the recognition result image and the recognition result table to a data processing module for processing and analysis;
the data processing module is used for obtaining the identification result tables of all reference students, further obtaining answer information of the test questions, respectively calculating scores of the objective questions and the subjective questions, obtaining the scores and the ranks of the single test paper of each student, and storing the scores and the ranks into the calculation result tables.
Further, the customized answer sheet module is used for generating positioning marks at the blank positions of at least three corners of the paper surface, and the positioning marks are used for confirming the scaling between the size of the actual printed test paper and the size of the original electronic test paper and carrying out deformation correction processing on the image of the electronic test paper;
the positioning information table includes: the positioning area, the school number area and the coordinate information corresponding to each test question.
Further, the coordinate information description table includes: the coordinate of the test paper positioning point, the coordinate of each objective question, the coordinate of each subjective question, the area coordinate of the school number, the coordinate of the privacy area to be covered during examination marking and the coordinate of the teacher scoring frame corresponding to each subjective question.
Further, the test paper question information comprises the total score of each objective question, correct answers, the number of options, the total score of each subjective question and the total score of the whole test paper.
Further, the image processing module performs rectification, and comprises the following steps: firstly, obtaining coordinate information L1 of a positioning point from a coordinate information table, amplifying the area of the coordinate area by m times to obtain L2, then cutting an L2 area from an original image obtained by a scanner to obtain an image I1, finding the positioning information of the positioning point in an actual image in the image I1 to obtain L3, obtaining the actual coordinate information corresponding to one positioning point, repeating the process, finding the coordinate information corresponding to all the positioning points in the answer sheet, and correcting the original image to the coordinates specified by an answer sheet design module through affine transformation.
Further, the image processing module performs handwritten image recognition, and comprises the following steps: and preprocessing the handwritten image, namely converting the image into a gray-scale image, carrying out binarization according to the Otsu method, then carrying out image denoising and enhancement processing, and finally converting the image into data acceptable by an identification model.
Further, the identifying data acceptable to the model includes converting pixel data in the image into a set of numbers having a length equal to an area of the image, each number representing a gray scale of a designated area of the image.
Further, the data processing module is further used for acquiring the questions that each student does not obtain the full score from the calculation results, collecting the answer images of the students and the scores of teachers, reading the images in batches, storing the results in a student wrong question table, and calculating the difficulty and the discrimination of the test paper and the positive answer rate of each test paper according to the score of each student single test paper.
Further, the data processing module is further configured to calculate a test paper difficulty coefficient:
for objective questions, the difficulty P is k/N, wherein k is the number of people answering the questions, and N is the total number of people participating in the test;
for subjective questions, the difficulty P is X/M, wherein X is the average score of the test questions; m is full score of test questions.
Further, the data processing module is further used for generating an enhanced exercise test paper for each student according to the test question condition of each student, wherein the difficulty and the knowledge point coverage of all test questions of the test paper are consistent with the question of the student who answers the wrong answer.
According to the intelligent marking paper marking system based on image recognition, provided by the embodiment of the invention, the teacher can write scores on the answer sheet by hands by pre-arranging the scoring frame on the answer sheet, and finally, the system scans to directly obtain the scores, ranking and other information of all students taking part in the examination, so that the operation process of the teacher is simplified, the students can know the wrong questions of the students, and then the purposes of really mastering knowledge are achieved by practicing similar questions to perform strengthening exercise. Aiming at the scene of a quick test, the traditional marking system needs to train teachers, intensively mark papers and solve the problem of inconvenience in use, a method of presetting a marking frame on an answer sheet enables teachers to directly mark and mark subjective questions on the answer sheet, and then the invention automatically identifies objective questions and the subjective questions and marks the scores of the subjective questions, and finally obtains the information of the students such as scores, ranking and the like. The teacher can quickly know the knowledge mastering condition of the students through the report, and the teacher can conveniently carry out targeted explanation, thereby simplifying the examination paper marking operation process and greatly reducing the learning cost of the teacher.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a block diagram of an intelligent marking scoring system based on image recognition according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an intelligent marking scoring system based on image recognition according to an embodiment of the present invention;
fig. 3 is a diagram illustrating an identification result table of a main topic according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
As shown in fig. 1 and fig. 2, the intelligent marking system based on image recognition according to the embodiment of the present invention includes: customized answer sheet module 100, image processing module 200 and data processing module 300.
Specifically, the customized answer sheet module 100 is configured to generate answer sheet information, and includes: the examination paper number, the two-dimensional code corresponding to the examination paper number, the positioning mark, the scoring frame, the basic information of the examinee, the input label of the general column, the objective question number and the filling area, and the positioning information table for describing the whole answer sheet information. Wherein, the positioning information table includes: the positioning area, the school number area and the coordinate information corresponding to each test question.
In an embodiment of the present invention, the customized answer sheet module 100 is configured to generate a test paper number and a two-dimensional code corresponding to the test paper number, and place the two-dimensional code at a fixed corner position of the answer sheet, where the test paper number has uniqueness. The customized answer sheet module 100 generates positioning marks at least three corner blanks of the sheet surface, and the positioning marks are used for confirming the scaling between the size of the actual printed test sheet and the size of the original electronic test sheet and carrying out deformation correction processing on the image of the electronic test sheet.
In addition, the customized answer sheet module 100 generates basic information of examinees and input labels of the general section, and places them at the title bar of the test paper; the basic information of the examinees comprises names, classes, grades, school numbers and the like. For subjective questions, a handwritten score scoring box is added in the appropriate place of the question (e.g., at the end of the question).
The image processing module 200 is used for scanning the two-dimensional code to obtain the test paper number. Specifically, the two-dimensional code and the test paper positioning points of the test paper are searched and identified at four corners of the test paper scanning image, and the two-dimensional code is converted into a test paper number. Then, the image processing module 200 obtains the corresponding coordinate information description table and the test paper question information according to the test paper number. Wherein, the coordinate information description table includes: the coordinate of the test paper positioning point, the coordinate of each objective question, the coordinate of each subjective question, the area coordinate of the school number, the coordinate of the privacy area to be covered during examination marking and the coordinate of the teacher scoring frame corresponding to each subjective question.
The test paper question information comprises the total score of each objective question, correct answers, the number of options, the total score of each subjective question and the total score of the whole test paper.
The image processing module 200 compares the actual coordinate position of the mark point of the scanned image with the mark point coordinate position recorded in the positioning information table, calculates the scale between the test paper scanned image and the test paper layout generated by typesetting, and then transforms and corrects the test paper layout to the previously designed size.
Specifically, the image processing module 200 performs rectification, which includes the following steps: firstly, acquiring coordinate information L1 of a positioning point from a coordinate information table, amplifying the area of the coordinate region by m times to obtain L2, and then cutting the L2 region from an original image obtained by a scanner to obtain an image I1. For example, m is 4, wherein the magnification m can be set according to the user's needs, and the above is only for the purpose of example.
The image processing module 200 finds the positioning information of the positioning point in the actual image in the image I1 to obtain L3, obtains actual coordinate information corresponding to one positioning point, repeats the process, finds coordinate information corresponding to all the positioning points in the answer sheet, and corrects the original image to the coordinates specified by the answer sheet design module through affine transformation.
And then, according to a coordinate information table generated by the answer sheet customizing module, cutting an image area corresponding to each test question into a privacy area, an objective question area, a subjective question area and a handwriting scoring frame area.
And for the objective question area, identifying the result filled by the student according to the number of the options and the coordinate information, drawing the identification result on an identification result image, and storing the identification result in an identification result table. The training model is to prepare handwritten material images, and then the handwriting material is trained by using caffe to obtain a recognition model.
For the subjective questions, a previously trained recognition model is called to recognize the handwritten numbers of the teacher in the scoring frame, recognition results are drawn on recognition result images, and the recognition results are stored in a recognition result table, as shown in fig. 3.
The image processing module 200 performs handwritten image recognition, and includes the following steps: preprocessing a handwritten image, namely converting the image into a gray-scale image, carrying out binarization according to the Otsu method, then carrying out image denoising and enhancement processing, and finally converting the image into a group of data acceptable by an identification model.
In embodiments of the invention, identifying data acceptable to the model comprises converting pixel data in the image into a set of numbers of length the area of the image, each number representing a gray scale of a designated area of the image to facilitate matching in the identification model to find the closest one.
For the subjective question area, calling a previously trained recognition model to recognize the handwritten image of the teacher in the scoring frame, drawing a recognition result on the recognition result image, and storing the recognition result image in a recognition result table; and sending the recognition result image and the recognition result table to the data processing module 300 for processing and analysis.
The data processing module 300 is configured to obtain the identification result tables of all the reference students, further obtain the test paper numbers from the identification result tables, obtain answer information of the test questions according to the test paper numbers, respectively calculate scores of the objective questions and the subjective questions, obtain a score and a rank of a single test paper of each student, and store the score and the rank in the calculation result tables.
Specifically, for objective questions, the answer information of the questions is compared with actual filling answers in a student identification result table, and the students are scored according to a set scoring strategy. For the subjective questions, the hand-reading scores of the students are taken from the student identification result table as the scores of the students on the test questions.
And summarizing all test question scores of each student, and calculating the score condition of a single test paper of each student. And calculating the total score of the students, the ranking information of the students in class and grade and the ranking condition of the student subjects in class and grade according to the scoring condition of each test paper of the students, and storing the calculation result into a calculation result table.
In addition, the data processing module 300 is further configured to obtain the question that each student does not obtain a full score from the calculation result, collect the answer images of the students and the score of the teacher, collect the reading images, store the result in the student wrong question table, and then calculate the difficulty, the discrimination and the positive answer rate of each test paper from the score of each single test paper of all students.
The data processing module 300 is further configured to calculate a test paper difficulty coefficient:
for objective questions, the difficulty P is k/N, wherein k is the number of people answering the questions, and N is the total number of people participating in the test;
for subjective questions, the difficulty P is X/M, wherein X is the average score of the test questions; m is full score of test questions.
The test paper discrimination coefficient calculation method includes the steps that reference students are sorted according to test paper scores, the highest 27% of the students are taken as high-grouping calculation difficulty coefficients to be PH, the last 27% of the students are taken as low-grouping calculation difficulty coefficients to be PL, and then the discrimination D of the test paper is PH-PL.
The question forward-answer rate calculating method is characterized in that the forward-answer rate P is the number of the answering test questions/the total number of the answering test questions.
Then, the data processing module 300 further derives a data analysis report according to the above data.
In addition, the invention can also provide accounts for students, can check test questions answered by mistake in the test of the past after logging in, and can print the test questions for retesting.
The data processing module 300 is further configured to generate an enhanced exercise test paper for each student according to the test question condition of each student, wherein the difficulty and knowledge point coverage of all test questions of the test paper are consistent with the question of the student who answers the wrong test paper, so as to achieve the purpose of enhanced exercise.
According to the intelligent marking paper marking system based on image recognition, provided by the embodiment of the invention, the teacher can write scores on the answer sheet by hands by pre-arranging the scoring frame on the answer sheet, and finally, the system scans to directly obtain the scores, ranking and other information of all students taking part in the examination, so that the operation process of the teacher is simplified, the students can know the wrong questions of the students, and then the purposes of really mastering knowledge are achieved by practicing similar questions to perform strengthening exercise. Aiming at the scene of a quick test, the traditional marking system needs to train teachers, intensively mark papers and solve the problem of inconvenience in use, a method of presetting a marking frame on an answer sheet enables teachers to directly mark and mark subjective questions on the answer sheet, and then the invention automatically identifies objective questions and the subjective questions and marks the scores of the subjective questions, and finally obtains the information of the students such as scores, ranking and the like. The teacher can quickly know the knowledge mastering condition of the students through the report, and the teacher can conveniently carry out targeted explanation, thereby simplifying the examination paper marking operation process and greatly reducing the learning cost of the teacher.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. An intelligent marking scoring system based on image recognition is characterized by comprising: a customized answer sheet module, an image processing module and a data processing module, wherein,
the customized answer sheet module is used for generating answer sheet information, and comprises: the examination paper number, the two-dimensional code corresponding to the examination paper number, the positioning mark, the scoring frame, the basic information of an examinee, the input label of a total column, the objective question number, the filling area and the positioning information table for describing the information of the whole answer sheet;
the image processing module is used for scanning the two-dimensional code to obtain the test paper number, obtaining a corresponding coordinate information description table and test paper question information according to the test paper number, comparing the actual coordinate position of the mark point of the scanned image with the mark point coordinate position recorded in the positioning information table, calculating the scale between the test paper scanned image and the test paper layout generated by typesetting according to the actual coordinate position, converting and correcting to the size designed previously; then, according to a coordinate information table generated by an answer sheet customizing module, cutting an image area corresponding to each test question into a privacy area, an objective question area, a subjective question area and a handwriting scoring frame area; for the objective question area, identifying the result filled by the student according to the number of options and the coordinate information, drawing the identification result on an identification result image, and storing the identification result in an identification result table; for the subjective question area, calling a previously trained recognition model to recognize the handwritten image of the teacher in the scoring frame, drawing a recognition result on the recognition result image, and storing the recognition result image in a recognition result table; sending the recognition result image and the recognition result table to a data processing module for processing and analysis;
the data processing module is used for obtaining the identification result tables of all reference students, further obtaining answer information of the test questions, respectively calculating scores of the objective questions and the subjective questions, obtaining the scores and the ranks of the single test paper of each student, and storing the scores and the ranks into the calculation result tables.
2. The intelligent marking and marking system based on image recognition as claimed in claim 1, wherein the customized answer sheet module is used for generating positioning marks at least three corner blanks of the paper surface, and the positioning marks are used for confirming the scaling ratio between the size of the actual printed test paper and the size of the original electronic test paper and the deformation correction processing of the electronic test paper image;
the positioning information table includes: the positioning area, the school number area and the coordinate information corresponding to each test question.
3. The intelligent image recognition-based marking scoring system as claimed in claim 1, wherein the coordinate information description table comprises: the coordinate of the test paper positioning point, the coordinate of each objective question, the coordinate of each subjective question, the area coordinate of the school number, the coordinate of the privacy area to be covered during examination marking and the coordinate of the teacher scoring frame corresponding to each subjective question.
4. The intelligent marking system based on image recognition as claimed in claim 1, wherein the test paper question information comprises total score of each objective question, correct answer, number of options, total score of each subjective question and total score of the whole test paper.
5. The intelligent image recognition-based marking scoring system as claimed in claim 1, wherein the image processing module performs rectification, comprising the steps of: firstly, obtaining coordinate information L1 of a positioning point from a coordinate information table, amplifying the area of the coordinate area by m times to obtain L2, then cutting an L2 area from an original image obtained by a scanner to obtain an image I1, finding the positioning information of the positioning point in an actual image in the image I1 to obtain L3, obtaining the actual coordinate information corresponding to one positioning point, repeating the process, finding the coordinate information corresponding to all the positioning points in the answer sheet, and correcting the original image to the coordinates specified by an answer sheet design module through affine transformation.
6. The intelligent image recognition-based marking scoring system as claimed in claim 1, wherein the image processing module performs handwritten image recognition, comprising the steps of: and preprocessing the handwritten image, namely converting the image into a gray-scale image, carrying out binarization according to the Otsu method, then carrying out image denoising and enhancement processing, and finally converting the image into data acceptable by an identification model.
7. The intelligent image recognition-based marking scoring system as recited in claim 6, wherein the data acceptable to the recognition model comprises converting pixel data in the image into a set of numbers having a length corresponding to an area of the image, each number representing a gray scale of a designated area of the image.
8. The intelligent marking paper-marking system based on image recognition as claimed in claim 1, wherein the data processing module is further configured to obtain the subject that each student does not obtain a full mark from the calculation result, collect the answer images of the students and the scores of the teachers, collect the reading images, store the results in a student wrong-answer table, and then calculate the difficulty, the discrimination and the positive answer rate of each test paper from the score of each test paper of all students.
9. The image recognition-based intelligent marking scoring system of claim 1, wherein the data processing module is further configured to calculate a test paper difficulty coefficient:
for objective questions, the difficulty P is k/N, wherein k is the number of people answering the questions, and N is the total number of people participating in the test;
for subjective questions, the difficulty P is X/M, wherein X is the average score of the test questions; m is full score of test questions.
10. The intelligent marking paper reading system based on image recognition as claimed in claim 1, wherein the data processing module is further configured to generate an enhanced exercise paper for each student according to the condition of the wrong answer test question of each student, wherein the difficulty and the knowledge point coverage of all the test questions of the paper are consistent with the wrong answer question of the student.
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| CN113408520A (en) * | 2021-07-16 | 2021-09-17 | 北京南昊科技股份有限公司 | Marking paper reading method, marking paper reading device and storage medium |
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| CN117037187A (en) * | 2023-10-10 | 2023-11-10 | 广东省科技基础条件平台中心 | Test paper image extraction method and device and terminal equipment |
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