Word Extraction and Recognition in Arabic Handwritten Text

Segmenting arabic manuscripts into text-lines and words is an important step to make recognition systems more efficient and accurate. The major problem making this task crucial is the word extraction process: first, words are often a succession of sub-words where the space value between these sub-words do not respect any rules. Second, the presence of connections even between non adjacent sub-words in the same text-line, makes word’s parts identification and the entire word extraction difficult. This work proposes an automatic system for arabic handwritten word extraction and recognition based on 1) localizing and segmenting touching characters, 2) extracting real subwords and structural features from word images and 3) recognizing them by a Markovian classifier. The performance of the proposed system is tested using samples extracted from historical handwritten documents. The obtained results are encouraging. We achieved an average rate of recognition of 87%.