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Handwritten answer recognition method and system based on deep learning

A technology of deep learning and recognition methods, applied in the field of image recognition, can solve the problems of low efficiency of manual correction of answers and limited graphics of recognized answers, and achieve the effects of avoiding misidentification, accurate comparison, and improving accuracy

Active Publication Date: 2020-12-01
SHANGHAI SQUIRREL CLASSROOM ARTIFICIAL INTELLIGENCE TECH CO LTD
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0003] The present invention provides a handwritten answer recognition method based on deep learning, which is used to solve the problems of low efficiency of the traditional method of manual correction of answers and limited answer graphics that can be recognized by the existing electronic marking method

Method used

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  • Handwritten answer recognition method and system based on deep learning
  • Handwritten answer recognition method and system based on deep learning
  • Handwritten answer recognition method and system based on deep learning

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Embodiment Construction

[0064] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0065] figure 1 It is a flow chart of Embodiment 1 of a handwritten answer recognition method based on deep learning provided by the present invention. Such as figure 1 As shown, the method includes the following steps:

[0066] S101: Obtain a handwritten answer image;

[0067] In this embodiment, the original image of the user's handwritten answer can be collected by a pre-set image collection device. Preferably, considering that there may be only a part of the answer area in the original image of the handwritten answer collected for the first time, and other places are blank areas and other irrelevant areas, this step can first collect the original image of the...

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Abstract

The invention discloses a handwritten answer recognition method and system based on deep learning, and belongs to the technical field of image recognition. The method comprises the following steps: collecting a handwritten answer image; calculating a comparison error amount between the handwritten answer image and each preset answer image in a preset answer image set; wherein the preset answer image set comprises a plurality of preset answer images; calculating the similarity between the handwritten answer image and each preset answer image in a preset answer image set according to the comparison error amount; and determining the preset answer image corresponding to the calculated maximum similarity as a final answer image to be output. The handwriting answer can be automatically recognized and output, and the recognition accuracy is high.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a method and system for recognizing handwritten answers based on deep learning. Background technique [0002] In schools or various examinations, the traditional way of correcting homework or grading papers is manually corrected. Obviously, these traditional ways of correcting answers consume a lot of manpower and material resources, not only time-consuming, but also inconvenient to manage. With the development of society, people's concept of time is getting stronger and stronger. On the one hand, it is required to shorten the answer review cycle, and on the other hand, it is also hoped that the document management and tracking of corrections will be more intelligent, thus creating a demand for automatic correction of handwritten answers. In the existing technology, for example, in large-scale examinations such as the college entrance examination, the machine can only r...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/20G06K9/34G06K9/62
CPCG06V40/33G06V10/22G06V10/267G06F18/22
Inventor 王鑫
Owner SHANGHAI SQUIRREL CLASSROOM ARTIFICIAL INTELLIGENCE TECH CO LTD
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