Abstract
In this paper, we present a multi-stage approach to handwritten Arabic text recognition using HMM where we separate the Arabic text image into core components and diacritics and recognize them separately using two separate HMM recognition systems. In the next stage, we combine the scores from both recognizers to make a final word hypothesis. This approach leads to huge reduction in the number of HMM models that need to be trained. Experiments conducted on a word recognition task using a publicly available benchmark database show the effectiveness of the technique. We achieve state-of-the-art results in addition to a compact model set for the recognition system.
Original language | English |
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Title of host publication | 13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings |
Publisher | IEEE Computer Society |
Pages | 751-755 |
Number of pages | 5 |
ISBN (Electronic) | 9781479918058 |
DOIs | |
State | Published - 20 Nov 2015 |
Publication series
Name | Proceedings of the International Conference on Document Analysis and Recognition, ICDAR |
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Volume | 2015-November |
ISSN (Print) | 1520-5363 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
Keywords
- Arabic text recognition
- Handwritten text recognition
- Model set reduction
- hidden Markov models
- multi-stage recognition
- rescoring
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition