Duration Models for Arabic Text Recognition using Hidden Markov Models
| Type of publication: | Inproceedings |
| Citation: | slim08:cimca |
| Booktitle: | International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 08), Vienna, Austria |
| Year: | 2008 |
| Pages: | 838--843 |
| URL: | http://www.hennebert.org/downl... |
| DOI: | 10.1109/CIMCA.2008.229 |
| Abstract: | We present in this paper a system for recognition of printed Arabic text based on Hidden Markov Models (HMM). While HMMs have been successfully used in the past for such a task, we report here on significant improvements of the recognition performance with the introduction of minimum and maximum duration models. The improvements allow us to build a system working in open vocabulary mode, i.e., without any limitations on the size of the vocabulary. The evaluation of our system is performed using HTK (Hidden Markov Model Toolkit) on a database of word images that are synthetically generated |
| Keywords: | HMM, image analysis, OCR |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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