Effect of Ghost Character Theory on Arabic Script Based Languages Character Recognition

International audienceArabic script is used by more than 1/4th population of the world in the form of different languages like Arabic, Persian, Urdu, Sindhi, Pashto etc but each language have its own words meaning. The set of شhas 58 alphabets. Arabic script based languages character recognition is difficult task due to complexities involved in this script not exist in other script. The analysis of the Arabic script is very complicated due to its use of diacritical marks associated with each character and written in many fonts and style. This script has gain very less intention by the researcher. This paper present a novel technique named Ghost Character Recognition Theory that will helps to develop a Multilanguage character recognition system for Arabic script based languages based on Ghost Character Theory. The main benefit of proposed approach is that it will works for all Arabic script based languages by doing effort for ghost character (basic skeleton) and developing dictionary for every language. By handling all Arabic script based languages many issues will arise like recognition rate as compared to system for specific languages, but in general it is not big issue for multilingual system and at the end we will get multilingual character recognition system

Human Reading Based Strategies for off-line Arabic Word Recognition

International audienceThis paper summarizes some techniques proposed for off-line Arabic word recognition. The point of view developed here concerns the human reading favoring an interactive mechanism between global memorization and local checking making easier the recognition of complex scripts as Arabic. According to this consideration, some specific papers are analyzed and their strategies commente

Offline printed Arabic character recognition

Optical Character Recognition (OCR) shows great potential for rapid data entry, but has limited success when applied to the Arabic language. Normal OCR problems are compounded by the right-to-left nature of Arabic and because the script is largely connected. This research investigates current approaches to the Arabic character recognition problem and innovates a new approach. The main work involves a Haar-Cascade Classifier (HCC) approach modified for the first time for Arabic character recognition. This technique eliminates the problematic steps in the pre-processing and recognition phases in additional to the character segmentation stage. A classifier was produced for each of the 61 Arabic glyphs that exist after the removal of diacritical marks. These 61 classifiers were trained and tested on an average of about 2,000 images each. A Multi-Modal Arabic Corpus (MMAC) has also been developed to support this work. MMAC makes innovative use of the new concept of connected segments of Arabic words (PAWs) with and without diacritics marks. These new tokens have significance for linguistic as well as OCR research and applications and have been applied here in the post-processing phase. A complete Arabic OCR application has been developed to manipulate the scanned images and extract a list of detected words. It consists of the HCC to extract glyphs, systems for parsing and correcting these glyphs and the MMAC to apply linguistic constrains. The HCC produces a recognition rate for Arabic glyphs of 87%. MMAC is based on 6 million words, is published on the web and has been applied and validated both in research and commercial use