Paper Info
Title
Real-time reading recognition of digital display instrument based on BP neural network.
Abstract
In many chemical industries, the metallurgy, a number of digital real-time monitoring instruments are used. The manual method will bring the problems of inefficient and misjudge. To recognize digital display instrument's real-time reading, a BP neural network is designed, an improved BP algorithm and fifteen feature extraction method is proposed. The image of instrument board is obtained by an digital camera firstly, then transmitted to PC, and an image preprocessing is carried through. The image preprocessing includes vertical tilt correction, binary, gray processing, grads sharp, noise point removal, character segmentation, character unitary adjustment, character contraction and feature extraction. Character features are extracted by n dimensions feature extraction method. Finally real-time recognize the reading by the BP neural network. The experiment results show that the recognition accurate rate is greater than 98%. And the time of recognizing for one instrument image is less than 0.5 second. © 2010 IEEE.
Year
DOI
Venue
2010
10.1109/ICCA.2010.5524437
ICCA
Keywords
Field
DocType
real time,feature extraction,neural networks,image processing,image segmentation,displays,neural network,chemical industry,artificial neural networks,pixel,image recognition,algorithm design and analysis,noise,backpropagation,neural nets
Computer vision,Computer science,Image processing,Display device,Feature extraction,Image segmentation,Digital camera,Artificial intelligence,Pixel,Backpropagation,Artificial neural network
Conference
Volume
Issue
ISSN
null
null
null
ISBN
Citations 
PageRank 
978-1-4244-5196-8
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Ruikun Gong122.14
Kui Yuan2586.58
Shanpo Nian300.68
Lei Chen422.14
Guangxiang Zhang5263.62
Yansong Tian621.46