Off-line recognition of text play a significant role in several application such as the automatic sorting of postal mail or editing old documents. It is the ability of the computer to distinguish characters and words. Automatic off-line recognition of text can be divided into the recognition of printed and handwritten characters. Off-line Arabic handwriting recognition still faces great challenges. This paper provides a survey of Arabic character recognition systems which are classified into the character recognition categories: printed and handwritten. Also, it examines the literature on the most significant work in handwritten text recognition without segmentation and discusses algorithms which split the words into characters.
Automatic off-line Arabic handwriting recognition still faces a big challenges. Due to the cursive nature of the Arabic language, most of published works are based on recognition of a whole word without segmentation. This paper presents a new framework for the recognition of handwritten Arabic words based on segmentation. This framework involves two phases (training phase and testing phase). In the training phase, Arabic handwritten characters were trained to be recognized, while in the testing phase, words were segmented into characters for recognition. Classification is achieved in two steps (classification of the segmented characters and classification of the word). A dictionary is constructed and used to correct any errors occurring during the previous stages of the recognition process. This work has been tested with IFN/ENIT database and a comparison made against some existing methods and promising results have been obtained.
Off-line recognition of text plays a significant role in several applications such as the automatic sorting of postal mail or editing old documents. The recognition of Arabic handwriting characters is a difficult task owing to the similar appearance of some different characters. Most researchers have presented methods that recognise isolated characters. However, recognition of all shapes of Arabic handwritten characters still remains a great challenge. The selection of the methods for feature extraction and classification remain the most important step in achieving high recognition accuracy. The purpose of this paper is to compare the effectiveness of DCT and DWT in capturing discriminative features of all shapes of Arabic handwritten characters including overlapping characters with ANN and HMM in the classification stage. Since, the recognition of handwritten characters is an important step in the recognition of a word after segmentation, this paper ascertains the effectiveness of these techniques in capturing useful information and, hence, achieving more accurate recognition results. This work has been tested with HACDB database containing 6,600 shapes of Arabic characters. The results have demonstrated that the feature extraction by DCT with ANN yields a higher recognition rate.
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