An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Recognition of Printed Arabic Text Using Neural Network Classifier
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
An Evolutionary Neuro-Fuzzy Approach to Recognize On-Line Arabic Handwriting
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applying Genetic Algorithms on Pattern Recognition: An Analysis and Survey
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
On-line Arabic handwriting recognition system based on visual encoding and genetic algorithm
Engineering Applications of Artificial Intelligence
Combining Slanted-Frame Classifiers for Improved HMM-Based Arabic Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR 2009 Arabic Handwriting Recognition Competition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Arabic Handwriting Recognition Using Restored Stroke Chronology
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Arabic Handwriting Recognition Using Projection Profile and Genetic Approach
SITIS '09 Proceedings of the 2009 Fifth International Conference on Signal Image Technology and Internet Based Systems
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This paper proposes and contributes towards designing a complete system for off-line Arabic character recognition. The proposed system is specifically meant for Arabic handwriting recognition, but it equally works for the typed character recognition. It has various phases including preprocessing and segmentation. It also includes thinning phase and finds vertical and horizontal projection profiles. The recognition phase is managed by genetic algorithm. The genetic algorithm stands on feature extraction algorithm that defines six features for each segment. The algorithm, for Arabic handwriting recognition, obtained 90.46 recognition rate. The proposed system has been compared with other systems in the literature. It has achieved the second best recognition rate.