Real-time Arabic handwritten character recognition
Pattern Recognition
Minimax entropy principle and its application to texture modeling
Neural Computation
Embedding Gestalt Laws in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Preprocessing and Structural Features for a Multi-Fonts Arabic/Persian OCR
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Combination of Pruned Kohonen Maps for On-line Arabic Characters Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
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The purpose of this study is to investigate handwritten online character recognition by Kohonen neural networks which learn class conditional Gibbs densities from training samples. The characters are represented by histograms (empirical distributions) of features. The Kohonen network learning algorithm implements a gradient ascent which maximizes an entropy criterion under constraints. Using a database of handwritten online Arabic characters produced without constraints by a large number of writers, we conducted extensive experiments which show the advantage of this Gibbsian Kohonen network over other classifiers such as a regular Kohonen neural network and a Gibbsian Bayes classifier.