Survey and bibliography of Arabic optical text recognition
Signal Processing
Artificial Neural Networks: A Tutorial
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Unconstrained handwriting recognition applied to the processing of bank cheques
Unconstrained handwriting recognition applied to the processing of bank cheques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Role of Holistic Paradigms in Handwritten Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Combination of Local and Global Vision Modelling for Arabic Handwritten Words Recognition
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
HMM Based Approach for Handwritten Arabic Word Recognition Using the IFN/ENIT- Database
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Improvement of the speech recognition in noisy environments using a nonparametric regression
International Journal of Parallel, Emergent and Distributed Systems
Off-line cursive script recognition: current advances, comparisons and remaining problems
Artificial Intelligence Review
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Given the number and variety of methods used for handwriting recognition, it has been shown that there is no single method that can be called the "best". In recent years, the combination of different classifiers and the use of contextual information .have become major areas of interest in improving recognition results. This paper addresses a case study on the combination of multiple classifiers and the integration of syntactic level information for the recognition of handwritten Arabic literal amounts. To the best of our knowledge, this is the first time either of these methods has been applied to Arabic word recognition. Using three individual classifiers with high level global features, we performed word recognition experiments. A parallel combination method was tested for all possible configuration cases of the three chosen classifiers. A syntactic analyzer makes a final decision on the candidate words generated by the best configuration scheme. The effectiveness of contextual knowledge integration in our application is confirmed by the obtained results.