DocumentCode :
3239799
Title :
Recognition of isolated handwritten Persian/Arabic characters and numerals using support vector machines
Author :
Mowlaei, Amir ; Faez, Karim
Author_Institution :
Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran, Iran
fYear :
2003
fDate :
17-19 Sept. 2003
Firstpage :
547
Lastpage :
554
Abstract :
We propose a system for recognition of isolated handwritten Persian/Arabic characters and numerals. Wavelet transform has been used for feature extraction in this system using Haar wavelet. The support vector machine (SVM), which is a new learning machine with very good generalization ability, and has been used widely in pattern recognition and regression estimation, uses as classifier in this system. The training and test patterns were gathered from various people with different ages and different educational backgrounds. The 32 characters in Persian language were categorized into 8 different classes in which characters of each class are very similar to each other. There are ten digits in Persian/Arabic languages where two of them are not used in zip codes in Iran. So, we have 8 different extra classes for digits. This system was used for recognizing the isolated handwritten postal addresses, which contain the name of cities and their zip codes. Our database contains 579 postal addresses in Iran. The system yields the recognition rate of 98.96% for these postal addresses. The results show an increment in recognition rates in comparison with our previous work in which we used the MLP neural network as classifier.
Keywords :
Haar transforms; handwritten character recognition; learning (artificial intelligence); neural nets; regression analysis; support vector machines; wavelet transforms; Haar wavelet; feature extraction; isolated handwritten Persian/Arabic characters recognition; learning machine; neural network; pattern recognition; regression estimation; support vector machine; support vector machines; wavelet transform; Character recognition; Cities and towns; Feature extraction; Handwriting recognition; Machine learning; Pattern recognition; Support vector machine classification; Support vector machines; Testing; Wavelet transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks for Signal Processing, 2003. NNSP'03. 2003 IEEE 13th Workshop on
ISSN :
1089-3555
Print_ISBN :
0-7803-8177-7
Type :
conf
DOI :
10.1109/NNSP.2003.1318054
Filename :
1318054
Link To Document :
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