Abstract
In the last decades, huge efforts have been made to develop automated handwriting recognition systems. The task of recognition usually involves several complex processes including image pre-processing, segmentation, features extracting and matching. This task usually gets harder by processing historical documents as they involve skews, document degradation and structure noise. Although, the success that has been achieved in English language, the recognition of handwritten Arabic still constitutes a major challenge for many reasons. The characteristic of Arabic language, as a Semitic language, differs from other languages (e.g., European languages) in several aspects such as complex structure, implicit characters, concatenation and, writing styles and direction. This work proposes a full recognition system for the task of word recognition from from Arabic historical documents. In the proposed system, a novel feature extraction method is presented to define robust features from Arabic words. Prior Feature extraction, each input image is pre-processed and segmented resulting in segmented words. After that, the features of each word/sub-word are defined based on Multiscale Convexity Concavity(MCC) analysis of contour word shape. For feature matching, a circular shift method is proposed to burn the computational cost instead of using traditional dynamic time warping (DTW) which exhibits high computational cost. Finally, the proposed algorithm has been evaluated under well-known dataset, namely, Ibn Sina, and showed high performance for historical documents with low computational cost.