Recognition of printed arabic text based on global features and decision tree learning techniques
Machine simulation of human reading has been the subject of intensive research for almost three decades. A large number of research papers and reports have already been published on Latin, Chinese and Japanese characters. However, little work has been conducted on the automatic recognition of Arabic in both on-line and off-line, has been achieved towards the automatic recognition of Arabic characters. This is a result of the lack of adequate support in terms of funding, and other utilities such as Arabic text databases, dictionaries, etc., and of course because of the cursive nature of its writing rules, and this problem is still an open research field. This paper presents a new technique for the recognition of Arabic text using the C4.5 machine learning system. The advantage of machine learning are twofold: it can generalize over the large degree of variations between different fonts and writing style and recognition rules can be constructed by examples. The technique can be divided into three major steps. The first step is digitization and pre-processing to create connected component, detect the skew of a document image and correct it. Second, feature extraction, where global features of the input Arabic word is used to extract features such as number of subwords, number of peaks within the subword, number and position of the complementary character, etc., to avoid the difficulty of segmentation stage. Finally, machine learning C4.5 is used to generate a decision tree for classifying each word. The system was tested with 1000 Arabic words with different fonts (each word has 15 samples) and the correct average recognition rate obtained using cross-validation was 92%.
printed arabic text global features connected component machine learning c4.5 cross-validation pattern recognition skew detection and correction structural classification