A Survey of Robust hybrid approach for Arabic character recognition

Abstract: In this paper we present a system of Arabic characters recognition dedicated to the automatic reading of ACR (Arabic Character Recognition). The developed system is a Fuzzy classifier: Fuzzy Logic (FL) combined with the Expert System (ES) to extract the topological and the contextual informations of each Print character. This combination is very useful to improve the powerful of Hybrid Intelligent Systems HIS in the field of OCR (Optical Character Recognition). The primary goal of this approach is to classify and to recognize all presented shapes. The training has used 180 descended pictures of the database of ACR. The Results gotten of ACR databases are promising. Keywords : Fuzzy Logic (FL), Hybrid Intelligent Systems (HIS), Expert System (ES), classification, Rules, Optical Character Recognition (OCR), Quantification Vector (QV), Artificial Intelligent (AI), Recognition, Training, and Inference. 1 Introduction The OCR (Optical Character recognition) systems for non-Latin script including Arabic are not commercially available and this is still an important area for research. A wide variety of approaches have been considered, but these can broadly be categorized into on-line and off-line techniques. On-line techniques record the movement of a pen on a drawing table and use its speed and direction of movement as part of the recognition process. Although on-line recognition tends to give better results than the off-line approaches, often the text to be recognised is only available as hard copy and therefore on-line recognition is not possible . The structural approach is a popular approach since it is widely used for Latin script, however while such techniques are successful for Latin script, the same approach is not as effective for cursive script such as Arabic character. Therefore, the recognition of non-Latin and Arabic script in particular has been far less extensively studied [1,2,3,34,35,]. Furthermore, the number of dots and their positions does not determine the meaning of Latin characters, but this is highly significant for many non-Latin scripts, in Arabic text, there may be one, two or three dots, which may be positioned above, below or in the middle of the character. The experience has shown that the most systems break down when the input document images are highly degraded, such as scanned images of carbon-copy documents, documents printed on low-quality paper, and documents that are n-th generation photocopies. Besides, the end user cannot compare the relative performances of the products because the various accuracy results are not reported on the same dataset. Therefore, the recognition rate of Arabic characters is lower than that of disconnected characters, such as printed English. More, Arabic characters are cursive in general and have the main features to characterise it [31,32,33 ], these earlier are not related with data acquisition, but also with the nature of the input. The nature of treatment is also based on the feature extraction aspects of image analysis to identify inherent characteristics [37,38,39,40]. In this context, we propose a hybrid intelligent system [7,10,11,12,16,24,25,20,26] for Arabic character 7o Simposio Argentino de Inteligencia Artificial - ASAI2005 Rosario, 29-30 de Agosto de 2005