Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/1756
Title: Combining slanted-frame classifiers for improved HMM-based Arabic handwriting recognition
Authors: Hajj Mohamad, Ramy Al
Likforman-Sulem, Laurence
Mokbel, Chafic 
Affiliations: Department of Electrical Engineering 
Keywords: Arabic handwriting
Word recognition
Feature extraction
IFN/ENIT database
Hidden Markov models
HMM
Neural network
Multilayer perceptrons
Classifier combination
Issue Date: 2009
Part of: IEEE transactions on pattern analysis and machine intelligence
Volume: 31
Issue: 7
Start page: 1165
End page: 1177
Abstract: 
The problem addressed in this study is the offline recognition of handwritten Arabic city names. The names are assumed to belong to a fixed lexicon of about 1,000 entries. A state-of-the-art classical right-left hidden Markov model (HMM)-based recognizer (reference system) using the sliding window approach is developed. The feature set includes both baseline-independent and baseline-dependent features. The analysis of the errors made by the recognizer shows that the inclination, overlap, and shifted positions of diacritical marks are major sources of errors. In this paper, we propose coping with these problems. Our approach relies on the combination of three homogeneous HMM-based classifiers. All classifiers have the same topology as the reference system and differ only in the orientation of the sliding window. We compare three combination schemes of these classifiers at the decision level. Our reported results on the benchmark IFN/ENIT database of Arabic Tunisian city names give a recognition rate higher than 90 percent accuracy and demonstrate the superiority of the neural network-based combination. Our results also show that the combination of classifiers performs better than a single classifier dealing with slant-corrected images and that the approach is robust for a wide range of orientation angles.
URI: https://scholarhub.balamand.edu.lb/handle/uob/1756
Open URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Electrical Engineering

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