Multi-stage HMM based Arabic text recognition with rescoring

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.