Abstract
The accuracy of handwritten word segmentation is essential for the recognition results; however, it is extremely complex task. In this work, an enhanced technique for Arabic handwriting segmentation is proposed. This technique is based on a recent technique which is dubbed in this work the base technique. It has two main stages: over-segmentation and neural-validation. Although the base technique gives promising results, it still suffers from many drawback such as the missed and bad segmentation-points(SPs). To alleviate these problems, two enhancements has been integrated in the first stage: word to sub-word segmentation and the thinned word restoration. Additionally, in the neural-validation stage an enhanced area concatenation technique is utilized to handle the segmentation of complex characters such as. Both techniques were evaluated using the IFN/ENIT database. The results show that the bad and missed SPs have been significantly reduced and the overall performance of the system is increased.