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Handwritten numeral recognition method and system

A digital recognition and handwriting technology, applied in the field of digital recognition, can solve the problems of inaccurate and single handwritten digital recognition

Inactive Publication Date: 2013-11-20
SUZHOU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of this, the embodiment of the present application provides a method and system for recognizing handwritten digits to solve the problem of inaccurate recognition of handwritten digits because only a single covariance feature can be used in the process of recognizing handwritten digits in the prior art

Method used

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  • Handwritten numeral recognition method and system
  • Handwritten numeral recognition method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0071] figure 1 This is a flowchart of a method for recognizing handwritten digits provided in Embodiment 1 of the present application.

[0072] like figure 1 As shown, the method includes:

[0073] S101. Receive a handwritten digital image input by a user.

[0074] In this embodiment of the present application, the user can input handwritten numbers on the touch screen of a tablet or a mobile phone by handwriting, and the method first receives an image of the handwritten numbers input by the user on the touch screen of a mobile phone or a tablet or other tool.

[0075] S102, extracting M covariance features of the handwritten digital image.

[0076] In this embodiment of the present application, the user may input at least one training image in advance, and extract M types of covariance features for each training image respectively, the number of covariances extracted for each training image is the same, and multiple training images There is a one-to-one correspondence be...

Embodiment 2

[0082] figure 2 A flowchart of a method for recognizing handwritten digits provided in Embodiment 2 of the present application.

[0083] like figure 2 As shown, the method includes:

[0084] S201. Receive a handwritten digital image input by a user.

[0085] S202, extracting M covariance features of the handwritten digital image.

[0086] The execution process of steps S201-S202 provided in the second embodiment of this application is the same as the execution process of steps S101-S102 provided in the first embodiment. For the detailed description of the steps S201-S202 provided in the second embodiment of this application, please refer to this embodiment of the application Steps S101-S102 in Step 1 will not be repeated here.

[0087] S203, respectively calculating the distance between each covariance feature of the handwritten digital image and the covariance feature of each preset training image corresponding to each covariance feature of the handwritten digital image...

Embodiment 3

[0110] Figure 4 The third embodiment of the present application provides a flow chart of a method for setting a process of preset M covariance features of each training image.

[0111] like Figure 4 As shown, the method includes:

[0112] S401. Receive at least one training image input by a user.

[0113] In this embodiment of the present application, at least one training image input by the user may be received in advance.

[0114] S402: Extract M covariance features of each training image respectively.

[0115] In this embodiment of the present application, the user may input at least one training image in advance, and extract M types of covariance features for each training image respectively, the number of covariances extracted for each training image is the same, and multiple training images There is a one-to-one correspondence between the extraction methods of M covariances of each training image in That is to say, 4 covariance features are extracted for each trai...

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Abstract

The invention provides a handwritten numeral recognition method and a handwritten numeral recognition device. The method comprises the following steps of: receiving a handwritten numeral image input by a user; extracting M covariance characteristics of the handwritten numeral image, wherein the value of M is any one positive integer which is greater than 1; and recognizing handwritten numerals according to the preset M covariance characteristics of each training image, a preset type tag carried by each training image and the M covariance characteristics of the handwritten numeral image. The handwritten numerals are recognized by extracting the M covariance characteristics of the handwritten numeral image, wherein the value of the M is any one positive integer which is greater than 1, so that the problem of inaccurate recognition of the handwritten numerals caused by adoption of a single covariance characteristic in the recognition process of the handwritten numerals in the prior art is solved.

Description

technical field [0001] The present application relates to the technical field of digital recognition, and in particular, to a method and system for recognizing handwritten digits. Background technique [0002] In recent years, with the rapid development of computer technology and digital image processing technology, handwritten digital recognition technology has been widely used in the fields of large-scale data statistics, mail sorting, finance, taxation and finance. As an important issue in the field of pattern recognition, handwritten digit recognition technology also has important theoretical value. Since numbers often represent precise values ​​in various fields, small errors are likely to bring unpredictable consequences. The accuracy rate has extremely high requirements. [0003] In the prior art, handwritten digits are often recognized by a Lie group K nearest neighbor classifier. The Lie group K nearest neighbor classifier takes the covariance of the regional image...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/68
Inventor 张莉周伟达王邦军何书萍杨季文李凡长
Owner SUZHOU UNIV
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