JPEG for Arabic Handwritten Character Recognition: Add a Dimension of Application

Abstract

The ultimate objective of any Optical Character Recognition (OCR) system is to simulate the human reading capabilities. That is why OCR systems are considered a branch of artificial intelligence and a branch of computer vision as well [Sunji Mori (1999)] Character recognition has received a lot of attention and success for Latin and Chinese based languages, but this is not the case for Arabic and Arabic-like languages such as Urdu, Persian, Jawi, Pishtu and others [Abdelmalek Z (2004)]. Researchers classify OCR problem into two domains. One deals with the image of the character after it is input to the system by, for instant, scanning in which is called Off-line recognition. The other has different input way, where the writer writes directly to the system using, for example, light pen as a tool of input. This is called On-line recognition. The online problem is usually easier than the offline problem since more information is available [Liana M & Venu G (2006)]. These two domains (offline & online) can be further divided into two areas according to the character itself that is either handwritten or printed character. Roughly, the OCR system based on three main stages: preprocessing, feature extraction, and discrimination (called also, classifier, or recognition engine) Figure 1.1 depicts the block diagram of the typical OCR system. Traditional OCR systems are suffering from two main problems, one comes from features extraction stage and the other comes from classifier (recognition stage). Feature extraction stage is responsible for extracting features from the image and passing them as global or local information to the next stage in order to help the later taking decision and recognizing the character. Two challenges are faced; if feature extractor extracts many features in order to offer enough information for classifier, this means many computations as well as more complex algorithms are needed. Thus, long processor time will be consumed. On the other hand, if few features are extracted in order to speed up the process, insufficient information may be passed to classifier. The second main problem that classifier is responsible for, is that most of classifiers are based on Artificial Neural Networks (ANNs). However, to improve the intelligence of these ANNs, huge iterations, complex computations, and learning algorithms are needed, which also lead to consume the processor time. Therefore, if the recognition accuracy is improved, the consumed time will increase and vice versa. O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m