Segmentation and Pre-Recognition of Arabic Handwriting
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline recognition of omnifont Arabic text using the HMM ToolKit (HTK)
Pattern Recognition Letters
Recognition of off-line printed Arabic text using Hidden Markov Models
Signal Processing
ARABIC CHARACTER RECOGNITION USING MODIFIED FOURIER SPECTRUM (MFS) VS. FOURIER DESCRIPTORS
Cybernetics and Systems
Accurate tool based on JPEG image compression for Arabic handwritten character shape recognition
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
HMM-based system for recognizing words in historical Arabic manuscript
International Journal of Robotics and Automation
Recognition of handwritten Arabic (Indian) numerals using Radon-Fourier-based features
ISPRA'10 Proceedings of the 9th WSEAS international conference on Signal processing, robotics and automation
The use of radon transform in handwritten Arabic (Indian) numerals recognition
WSEAS Transactions on Computers
Off-line handwritten arabic word recognition using SVMs with normalized poly kernel
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
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Hidden Markov Models (HMM) have been used with some success in recognizing printed Arabic words. In this paper, a complete scheme for totally unconstrained Arabic handwritten word recognition based on a Model discriminant HMM is presented. A complete system able to classify Arabic-Handwritten words of one hundred different writers is proposed and discussed. The system first attempts to remove some of variation in the images that do not affect the identity of the handwritten word. Next, the system codes the skeleton and edge of the word so that feature information about the lines in the skeleton is extracted. Then a classification process based on the HMM approach is used. The output is a word in the dictionary. A detailed experiment is carried out and successful recognition results are reported.