Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Chinese character image stroke extraction method and system based on fully convolutional neural network

A convolutional neural network and Chinese character technology, applied in the field of computer vision and pattern recognition, can solve problems such as difficulty in extracting strokes of handwritten characters, and achieve the effects of avoiding extraction omissions or redundancy, saving labor costs, and saving calculations

Active Publication Date: 2021-02-02
INST OF AUTOMATION CHINESE ACAD OF SCI
View PDF8 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0013] In order to solve the above-mentioned problems in the prior art, that is, the difficult problem of free-writing handwritten character stroke extraction, the present invention provides a Chinese character image stroke extraction method based on a fully convolutional neural network. The extraction method includes:

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
  • Chinese character image stroke extraction method and system based on fully convolutional neural network
  • Chinese character image stroke extraction method and system based on fully convolutional neural network
  • Chinese character image stroke extraction method and system based on fully convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0061] The application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain related inventions, not to limit the invention. It should also be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.

[0062] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.

[0063] A method for extracting strokes of Chinese character images based on a fully convolutional neural network of the present invention, comprising:

[0064] Step S10, obtaining a Chinese character image as an input image;

[0065] Step S20, extracting the overlapping area map of...

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 belongs to the field of computer vision and pattern recognition, and specifically relates to a method and system for extracting strokes of Chinese character images based on a fully convolutional neural network, aiming to solve the problem of difficulty in extracting strokes of handwritten characters for free writing. The method of the present invention includes: extracting the region of the acquired Chinese character image; performing skeletonization operation on the overlapping region and non-overlapping region; calculating the coherence between any stroke segments in the skeletonized overlapping region; Stroke segments that belong to the same stroke are connected, and the stroke segments that are directly connected in the non-overlapping area are merged into a complete skeleton shape stroke. On the one hand, the present invention can still realize the stroke extraction of handwritten Chinese characters when the strokes of free-writing handwritten Chinese characters overlap; , greatly saving labor costs.

Description

technical field [0001] The invention belongs to the field of computer vision and pattern recognition, and in particular relates to a method and system for extracting strokes of Chinese character images based on a fully convolutional neural network. Background technique [0002] Stroke extraction from Chinese character images plays an important role in text recognition research and related applications based on structural analysis. Single-character classification of Chinese handwritten / printed characters based on deep learning technology has achieved a fairly high accuracy rate. However, in many applications, people not only care about the classification of characters, but also pay attention to stroke interpretation, writing quality evaluation, shape beautification, and font design. And other problems, and this requires segmentation and extraction of strokes in text images. [0003] For the problem of stroke extraction of offline Chinese characters, there are mainly two type...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06N3/04
CPCG06V30/2268G06N3/045
Inventor 刘成林王铁强
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com