A two-tier Arabic offline handwriting recognition based on conditional joining rules

In this paper we present a novel approach for the recognition of off-line Arabic handwritten text motivated by the Arabic letters' conditional joining rules. A lexicon of Arabic words can be expressed in terms of a new alphabet of PAWs (Part of Arabic Word). PAWs can be expressed in terms of letters. The recognition problem is decomposed into two problems to solve simultaneously. To find the best matching word for an input image, a Two-Tier Beam search is performed. In Tier One, the search is constrained by a letter to PAW lexicon. In Tier Two, the search is constrained by a PAW to word lexicon. The searches are driven by a PAW recognizer. Experiments conducted on the standard IFN/ENIT database [6] of handwritten Tunisian town names show word error rates of about 11%. This result compares to the results of the commonly used HMM based approaches.