NDLI: Using Single-Layer neural network for recognition of isolated handwritten Persian digits

Content Provider	IEEE Xplore Digital Library
Author	Pourmohammad, A. Ahadi, S.M.
Copyright Year	2009
Description	Author affiliation: Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran (Pourmohammad, A.; Ahadi, S.M.)
Abstract	Recently a new method for recognition of isolated handwritten Persian digits, based on support vector machines (SVMs), has been introduced. In this research, this method was implemented for the same task with three new modifications, i.e. only one popular shape was considered for digits written in different shapes; sizes of glyphs normalized to digit boundaries; MLP (Multi-Layer Perceptron), SVM/MLP and SLP (Single-Layer Perceptron) neural networks used for classification. Each digit is considered from 4 different views, and from each view, 50 features were extracted to obtain 200 features. Multiple SVM classifiers were trained to separate different classes of digits for MLP and were compared with ordinary training for MLP and SLP. Experiments on real life samples from AMIRKABIR database showed that the proposed features, when used with SLP, could perform very well leading to a recognition rate of 98.3%, compared with 94.14% obtained from MLP classifier with SVM training using the CENPARMI database.
Starting Page	1
Ending Page	4
File Size	770109
Page Count	4
File Format	PDF
ISBN	9781424446568
DOI	10.1109/ICICS.2009.5397666
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Handwriting recognition Support vector machines Shape Support vector machine classification Spatial databases Feature extraction Colored noise Noise shaping Pattern recognition Persian Handwritten Digits Recognition component Pattern Recognition Neural Networks Optical Character recognition (OCR) Multiple Support Vector Classifier
Content Type	Text
Resource Type	Article

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