Development of an efficient neural-based segmentation technique for Arabic handwriting recognition

Off-line Arabic handwriting recognition and segmentation has been a popular field of research for many years. It still remains an open problem. The challenging nature of handwriting recognition and segmentation has attracted the attention of researchers from industry and academic circles. Recognition and segmentation of Arabic handwritten script is a difficult task because the Arabic handwritten characters are naturally both cursive and unconstrained. The analysis of Arabic script is more complicated in comparison with English script. It is believed, good segmentation is one reason for high accuracy character recognition. This paper proposes and investigates four main segmentation techniques. First, a new feature-based Arabic heuristic segmentation AHS technique is proposed for the purpose of partitioning Arabic handwritten words into primitives (over-segmentations) that may then be processed further to provide the best segmentation. Second, a new feature extraction technique (modified direction features-MDF) with modifications in accordant with the characteristics of Arabic scripts is also investigated for the purpose of segmented character classification. Third, a novel neural-based technique for validating prospective segmentation points of Arabic handwriting is proposed and investigated based on direction features. In particular, the vital process of handwriting segmentation is examined in great detail. The classifier chosen for segmentation point validation is a feed-forward neural network trained with the back-propagation algorithm. Many experiments were performed, and their elapsed CPU times and accuracies were reported. Fourth, new fusion equations are proposed and investigation to examine and evaluate a prospective segmentation points by obtaining a fused value from three neural confidence values obtained from right and center character recognition outputs in addition to the segmentation point validation (SPV) output. Confidence values are assigned to each segmentation point located through feature detection. All techniques components are tested on a local benchmark database. High segmentation accuracy is reported in this research along with comparable results for character recognition and segmentation.