Test paper handwritten English character recognition method and system based on deep learning

A technology of character recognition and deep learning, applied in the field of image recognition, can solve problems such as unrecognizable words, weak interpretability, complicated process of feature extraction, etc., achieve good segmentation effect, improve accuracy, and improve accuracy

Active Publication Date: 2020-09-08
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method extracts visual features, which are highly interpretable, but the process of extracting features is relatively complicated.
[0009] (2) The basic idea of ​​the recognition method based on statistical features is to extract the moment feature of the character or word image, perform K-L transform or Fourier transform on it to generate global features such as feature vectors, and transform the local grayscale of the image , projection, gradient and other local features for classification and recognition, but the interpretability of such features

Method used

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  • Test paper handwritten English character recognition method and system based on deep learning
  • Test paper handwritten English character recognition method and system based on deep learning
  • Test paper handwritten English character recognition method and system based on deep learning

Examples

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Embodiment 1

[0057] Such as figure 1 As shown, Embodiment 1 of the present disclosure provides a method for recognizing handwritten English characters in test papers based on deep learning, including the following steps:

[0058] Step (1): Obtain the test paper image to be recognized, and cut out the words in the test paper image.

[0059] Step (2): Use a deep neural network to recognize word images.

[0060] The concrete process of described step (1) is:

[0061] Step (1.1): Carry out binarization operation on the test paper image.

[0062] First, the original test paper image is converted into a grayscale image. According to the characteristics of the scanned image of the original test paper, use the component method to select one of the three color channels of the image; then use the OTSU algorithm to convert it into a binary image.

[0063] Step (1.2): Slice the lines of text in the test paper image.

[0064] The detailed process is as follows:

[0065] Step (1.2.1): Calculate the...

Embodiment 2

[0106] Embodiment 2 of the present disclosure provides a system for recognizing handwritten English characters in test papers based on deep learning, including:

[0107] The data acquisition module is configured to: acquire the image of the test paper to be identified;

[0108] The data processing module is configured to: cut the acquired image to obtain the word image in the test paper image;

[0109] The recognition module is configured to: utilize the trained neural network model based on the attention mechanism to recognize the word image, and obtain the word recognition result;

[0110] Wherein, cutting the obtained image is specifically performing a binarization operation on the test paper image, cutting the text line in the test paper image, and cutting the English words in the text line image.

[0111] The working method of the system is the same as that of the deep learning-based handwritten English character recognition method in Embodiment 1, and will n...

Embodiment 3

[0113] Embodiment 3 of the present disclosure provides a medium on which a program is stored, and when the program is executed by a processor, the steps in the method for recognizing handwritten English characters in an examination paper based on deep learning as described in Embodiment 1 of the present disclosure are implemented. The steps are:

[0114] Obtain the image of the test paper to be recognized;

[0115] Cut the obtained image to obtain the word image in the test paper image;

[0116] Use the trained neural network model based on the attention mechanism to recognize the word image and get the word recognition result;

[0117] Wherein, cutting the obtained image is specifically performing a binarization operation on the test paper image, cutting the text line in the test paper image, and cutting the English words in the text line image.

[0118] The detailed steps are the same as those of the deep learning-based handwritten English character recognition...

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PUM

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Abstract

The invention provides a test paper handwritten English character recognition method and device based on deep learning, and belongs to the technical field of image recognition. The method comprises the steps of acquiring a to-be-recognized test paper image; cutting the acquired image to obtain a word image in the test paper image, and identifying the word image by using a trained neural network model based on an attention mechanism to obtain a word identification result; wherein the step of cutting the acquired image specifically comprises the sub-steps of carrying out binarization operation on the test paper image, cutting text lines in the test paper image, and cutting English words in the text line image; according to the method, the English text line segmentation method based on dynamic line segmentation and the word sequence recognition method based on the attention mechanism are adopted, a good segmentation effect is achieved on the bent text, and the word recognition accuracy iseffectively improved.

Description

technical field [0001] The present disclosure relates to the technical field of image recognition, in particular to a method and system for recognizing handwritten English characters in test papers based on deep learning. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] The recognition of handwritten English characters in the test paper includes two steps. One is to cut out the English words in the test paper, and the second step is to recognize the cut out word pictures and transcribe them into word texts. [0004] Word segmentation is usually performed in two steps: first the segmentation of the text line, and then the segmentation of the words in the text line. [0005] (1) The basic idea of ​​​​segmentation of text lines is to find the starting point of segmentation first, and then find the dividing line with methods such as connecte...

Claims

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Application Information

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IPC IPC(8): G06K9/34G06K9/68G06N3/04G06N3/08
CPCG06N3/049G06N3/08G06V30/153G06V30/2445G06N3/045
Inventor 许信顺蔡文睿罗昕
Owner SHANDONG UNIV
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