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

Convolutional neural network-based character recognition method

A convolutional neural network, character recognition technology, applied in the field of pattern recognition, can solve problems such as the inability to achieve high accuracy

Active Publication Date: 2018-09-28
WUHAN UNIV
View PDF2 Cites 39 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, for poor-quality and complex drawings, traditional recognition algorithms based on convolutional neural networks cannot achieve high accuracy.

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
  • Convolutional neural network-based character recognition method
  • Convolutional neural network-based character recognition method
  • Convolutional neural network-based character recognition method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0063] The technical solutions and effects of the present invention will be further described in detail below with reference to the accompanying drawings.

[0064] refer to figure 1 , the implementation steps of a character recognition method based on a convolutional neural network provided by an embodiment of the present invention are as follows:

[0065] Step 1: Use the optimal threshold processing based on the Ostu method to binarize the scanned documents of engineering drawings, and separate the graphics and text in the scanned documents of engineering drawings from the background;

[0066] Specific steps are as follows:

[0067] Use {0, 1, 2, ..., L-1} to represent L different gray levels in a scanned image, calculate the normalized histogram of the input image, and use p i , i=0,1,2,...,L-1 represents the histogram, assuming a threshold k is selected, the image is divided into C at k 0 ={0,1,...,k} and C 1 ={k+1,k+2,...,L-1} two groups, then the pixels are divided in...

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 discloses a convolutional neural network-based character recognition method, and mainly aims at solving the problem that existing manual engineering drawing data input is time-consuming,labor-consuming and high in error rate. The method comprises the following main steps of: 1) converting grayscale images into binary images by adoption of Otsu method-based optimal global threshold value processing; 2) rotating to correct inclined images; 3) extracting a table box by adoption of a mathematic morphological algorithm; 4) obtaining an area where characters are located; 5) rotating inclined character strings for a corresponding angle to a horizontal direction; 6) searching circles in the images by adoption of a circular Hough transformation algorithm, and extracting welding beadnumbers in an engineering drawing; 7) carrying out image-text recognition on the engineering drawing by adoption of a convolutional neural network method; 8) correcting simple errors through an encoding rule and automatically correcting recognition errors; and 9) outputting and storing the drawing data. In image-text recognition of engineering drawings, the method has high correctness and timeliness, and is capable of realizing efficient recording and management of engineering drawing data.

Description

technical field [0001] The invention belongs to the field of pattern recognition, in particular to a character recognition method based on a convolutional neural network, which can be used for document scanning and recognition of industrial drawings. Background technique [0002] Optical Character Recognition (OCR) technology refers to the process in which electronic devices check characters printed on paper, determine their shapes by detecting dark and bright patterns, and then use character recognition to translate the shapes into computer text. OCR is an important research direction in the field of pattern recognition, and is widely used in automatic information processing, and has important practical significance in engineering fields such as architecture and machinery. [0003] In actual engineering, the staff need to enter the information of industrial drawings into the computer for the recording and management of engineering information. However, under normal circums...

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 Applications(China)
IPC IPC(8): G06K9/00G06K9/34
CPCG06V30/422G06V30/158G06V30/10
Inventor 张海剑成帅杨天韵
Owner WUHAN UNIV
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