Test paper correcting method and device

Abstract

The invention provides a test paper correcting method and a test paper correcting device. The test paper correction method comprises the steps of obtaining a test question image and an answering arearule; determining an answering area image according to the answering area rule and the test question image; identifying answer text information in the answer area image; comparing the answer text information with standard answer information corresponding to the test question image; correcting the test paper according to a comparison result of the answer text information and the standard answer information. Therefore, intelligent correction of paper test paper objective questions is realized; the problem that in a common examination process, if no answer sheet exists, paper can only be manuallymarked by a teacher is avoided; the time of teachers can be greatly saved, the test paper correcting efficiency is improved, errors are reduced; compared with the prior art, correction can be more flexibly carried out in a fine-grained mode, meanwhile, the problem that multiple sub-answers exist in one question is solved, the correcting accuracy is greatly improved, and the correcting efficiencyand subject flexibility are improved.

Description

technical field [0001] The invention relates to the technical field of test paper correction, in particular to a test paper correction method and a test paper correction device. Background technique [0002] With the widespread application of artificial intelligence in the field of education, more and more methods of intelligent marking and intelligent correction emerge in an endless stream. The current intelligent corrections can be roughly divided into the following three categories: corrections based on string matching, precise corrections based on confusion rules, and coarse-grained corrections based on sample sets using machine learning algorithms. Among them, string-based matching cannot distinguish some synonymous sub-answers well; accurate correction based on confusing rules requires manual setting of rules, and there will be some places that are not well thought out. The definition of the rules directly affects the correction. The result; coarse-grained correction ...

AI Technical Summary

Problems solved by technology

Among them, string-based matching cannot distinguish some synonymous sub-answers well; precise correction based on confusing rules requires manual setting of rules, and some places are not well thought out. The definition of rules directly affects the correction Results; coarse-grained correction based on deep learning algorithms requires a large number of training sample sets, and is not very portable. If the subject title changes, the model needs to be retrained

[0003] At present, no matter what kind of intelligent correction method is used, there are some problems, that is, it cannot flexibly meet the paper-based correction of filling-in-the-blank questions in various disciplines.

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