Abstract

This paper describes recent advances in hidden Markov model (HMM) based OCR for machine-printed arabic documents. A combination of script-independent and script-specific techniques are applied to glyph models and language models (LM). Script-independent techniques we applied are higher order n-gram LMs for N-best rescoring and discriminative estimation of glyph HMMs. Arabic specific techniques include the use of context-dependent HMMs for glyph modeling and Parts-of-Arabic-Words in language modeling. We present experimental results that demonstrate a 40% relative reduction in word error rate over the baseline configuration on a corpus of machine-printed Arabic documents.

Date

2008

Authors

Rohit Prasad, Shirin Saleem, Matin Kamali, Ralf Meermeier, Prem Natarajan

Conference

2008 19th International Conference on Pattern Recognition

Pages

1-4

Publisher

IEEE