DocumentCode :
1625480
Title :
Efficient Optical Character Recognition on Graphics Processing Unit
Author :
Arianyan, E. ; Motamedi, Seyed Ahmad ; Arianyan, I.
Author_Institution :
Educ. & Res. Inst. for ICT, Tehran, Iran
fYear :
2012
Firstpage :
789
Lastpage :
793
Abstract :
Optical Character Recognition (OCR) is a technique by the help of which the optical characters are identified automatically by a computer. There are many methods for OCR, one of which is neural network that we use. Unfortunately, the long training and testing time of these networks is disturbing, but we healed this problem by mapping our network on graphics card by using Jacket which is the product of Accelereyes group. By so doing, we achieved the speedup of up to twelve factors. Graphics Processing Units (GPUs) have parallel structure containing many cores capable of running thousands of threads in parallel. We train a multi layer perceptron network using back propagation rule which has a degree of parallelism that is suitable for implementation on new graphics card. We examine the Persian characters that are typed on the new system of Farsi license plates to make a database of characters uses in this system and apply them as train and test data for our network.
Keywords :
backpropagation; graphics processing units; neural nets; optical character recognition; Accelereyes group; Farsi license plates; GPU; Jacket; OCR; Persian characters; backpropagation rule; graphics card; graphics processing unit; many cores; multilayer perceptron network; network mapping; neural network; optical character recognition; parallel structure; Biological neural networks; Character recognition; Graphics processing units; Neurons; Optical character recognition software; Training; CUDA; GPU; Jacket; Neural network; OCR;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Telecommunications (IST), 2012 Sixth International Symposium on
Conference_Location :
Tehran
Print_ISBN :
978-1-4673-2072-6
Type :
conf
DOI :
10.1109/ISTEL.2012.6483093
Filename :
6483093
Link To Document :
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