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.