Classification of Personal Arabic Handwritten Documents

This paper presents a novel holistic technique for classifying Arabic handwritten text documents. The classification of Arabic handwritten documents is performed in several steps. First, the Arabic handwritten document images are segmented into words, and then each word is segmented into its connected parts. Second, several structural and statistical features are extracted from these connected parts and then combined to represent a word with one consolidated feature vector. Finally, a generalized feedforward neural network is used to learn and classify the different styles/fonts into word classes, which are used to retrieve Arabic handwritten text documents. The extraction of structural and statistical features from the individual connected parts as compared to the extraction of these features from the whole word improved the performance of the system. Key-Words: Data mining of Arabic text, Word recognition, Arabic handwriting, Segmentation of Arabic handwritten documents, Feature extraction, Classification, and Retrieval of Arabic handwritten documents