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Several segmentation approaches proposed in the past decades for Arabic handwritings suffer from over-segmentation. This problem decomposes a single letter into small strokes. The aim of this work is to handle this problem using Artificial Neural Networks with a set of combination rules to keep the correct strokes (letters) and combine the over-segmented ones to intact letters in a correct way. After word segmentation, the resulting segments are normalized. Then, a set of features was extracted from each segment and passed to Artificial Neural Network to be recognized. Finally, proposed combination rules were applied to unrecognized strokes and to specific recognized letters. The success rate of the experimental results exceeds 95%.

Recognition-based Segmentation of Arabic Handwriting