Abstract
Hidden Markov Model (HMM) is one of the most widely used classifier for text recognition. In this paper we are presenting novel sub-character HMM models for Arabic text recognition. Modeling at sub-character level allows sharing of common patterns between different contextual forms of Arabic characters as well as between different characters. The number of HMMs gets reduced considerably while still capturing the variations in shape patterns. This results in a compact and efficient recognizer with reduced model set and is expected to be more robust to the imbalance in data distribution. Experimental results using the sub-character model based recognition of handwritten Arabic text as well printed Arabic text are reported.
Original language | English |
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Article number | 6628700 |
Pages (from-to) | 658-662 |
Number of pages | 5 |
Journal | Proceedings of the International Conference on Document Analysis and Recognition, ICDAR |
DOIs | |
State | Published - 2013 |
Bibliographical note
Funding Information:The German Research Council (DFG) funded this project. We would like to thank G. Fuchs for comments on regional geology. S.P. Singh kindly assisted the fieldwork. We would like to thank C.T. Klootwijk and two anonymous reviewers for constructive comments on an earlier draft of the manuscript. F. Martinez-Hernandez kindly commented on the results of the remanence anisotropy measurements. [RV]
Keywords
- Arabic text recognition
- Hidden Markov Models
- OCR
- Parameter sharing
- Sub-character HMMs
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition