Method for extracting characteristic of handwritten Chinese character image

A feature extraction, Chinese character technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problem that the positioning cannot extract scale-invariant features, and achieve the effect of improving the recognition performance

Inactive Publication Date: 2009-06-03
SOUTH CHINA UNIV OF TECH
View PDF0 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to overcome the problem that the direct application of SIFT feature point positioning cannot extract effective scale-invariant features suitable for different writing styles, combining the characteristics of handwritten Chinese characte

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for extracting characteristic of handwritten Chinese character image
  • Method for extracting characteristic of handwritten Chinese character image
  • Method for extracting characteristic of handwritten Chinese character image

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0032] The flow chart of the dynamic gradient statistical feature extraction method of the present invention is as attached image 3 As shown, specifically, the input Chinese character image is divided into an elastic grid on the one hand to obtain 64 sub-regions, and then the center point of the sub-region is determined as a seed point, and a gradient statistical vector is assigned to each seed point. On the other hand, Obtain the gradient direction vector of each pixel in the image, obtain the gradient information of each pixel by decomposing the gradient vector, and then add the gradient information of each pixel to the seed points of the adjacent subregions according to the rules, and then add Each statistical vector is normalized, and finally the statistical vector is sequentially spliced ​​into the final feature vector output.

[0033] The schematic diagram of the adjacent sub-region mentioned in step (3) of the feature extraction method of the present invention is as attach...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a method for extracting characteristic of a handwritten Chinese character image. The global handwritten Chinese character image is used as a characteristic extraction area; furthermore, a Chinese character image area is divided by an elastic network; scale invariability characteristic conversion method is employed to carry out dynamic statistics of gradient direction information of relative areas on each network, thus gaining the characteristic of a handwritten Chinese character. The method randomly selects 500 samples from a HCL2000 handwritten Chinese character sample database to carry out a training and selects 200 non-repeated samples to carry out a recognition test; the recognition result when the method is used for gaining characteristic is that the hit ratio of a firstly selected character is 96.061% and the hit ratio of the first 10 candidate characters is 99.688%.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and artificial intelligence, in particular to a handwritten Chinese character image recognition processing method. technical background [0002] A handwritten Chinese character recognition system is divided into four modules: preprocessing, feature extraction, classification recognition, and postprocessing. Feature extraction is considered to be one of the key steps in Chinese character recognition and has an important impact on the final performance of the entire system. In recent years, many scholars have done a lot of research work on how to obtain effective features, and achieved many excellent results. The Gabor feature is one of the more effective features of various Chinese characters, and its application has a good biological vision theory support behind it. In fact, pattern recognition has always been closely related to computer vision and biological vision theory. [0003] ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06K9/46G06K9/62
Inventor 金连文张志毅丁凯
Owner SOUTH CHINA UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products