Arabic OCR Segmented - based System Hassanin

A new investigation in the Arabic OCR system has presented for the offline recognition of machineprinted cursive words. Therefore, a reliable transformation mechanism will be used to transform image text into free text (ASCII or Unicode Texts), that can be directly searched by a computer. Therefore, traditional preprocessing model (segmentation phase) will be included to extract each word from image text and divide it into segments. Then, recognition phase will take place, to find the most likelihoods of each possible text/character class given the segments. Accordingly, many classifiers can be used such as neural networks, Naïve Bayes, HMM classifiers. Such likelihoods are used to feed special algorithm as input in such ways to recognize the entire word. The whole process of the proposed framework includes three main stages: preparation, training, and testing. The data preparation aims at scanning, data image selection, alignment, identify text regions, and separate non text or image regions. Second, the training stage takes place, to extract features and build up the related language model; such features will be used in the third stage. Accordingly, at the first stage the paper focuses on the techniques used for font sizing, binarization, skewing, clearing (denoising), and segmentation before recognition takes place. [Hassanin M. Al-Barhamtoshy and Mohsen A. Rashwan. Arabic OCR Segmented-based System. Life Sci J 2014;11(10):1273-1283]. (ISSN:1097-8135). http://www.lifesciencesite.com. 200