摘要
The Arabic Language has a very rich vocabulary. More than 200 million peoplespeak this language as their native speaking, and over 1 billion people use it in severalreligion-related activities. In this paper a new technique is presented for recognizing printedArabic characters. After a word is segmented, each character/word is entirely transformed into afeature vector. The features of printed Arabic characters include strokes and bays in variousdirections, endpoints, intersection points, loops, dots and zigzags. The word skeleton is decomposedinto a number of links in orthographic order, and then it is transferred into a sequence of symbolsusing vector quantization. Single hidden Markov model has been used for recognizing the printedArabic characters. Experimental results show that the high recognition rate depends on the number ofstates in each sample.
The Arabic Language has a very rich vocabulary. More than 200 million peoplespeak this language as their native speaking, and over 1 billion people use it in severalreligion-related activities. In this paper a new technique is presented for recognizing printedArabic characters. After a word is segmented, each character/word is entirely transformed into afeature vector. The features of printed Arabic characters include strokes and bays in variousdirections, endpoints, intersection points, loops, dots and zigzags. The word skeleton is decomposedinto a number of links in orthographic order, and then it is transferred into a sequence of symbolsusing vector quantization. Single hidden Markov model has been used for recognizing the printedArabic characters. Experimental results show that the high recognition rate depends on the number ofstates in each sample.
