Recognition of Off-line Arabic Handwriting words Using HMM Toolkit (HTK)

There are a lot of difficulties facing a good handwritten Arabic recognition system such as the similarities of different character shapes and the unlimited variants in human handwriting. This paper presents a handwriting Arabic word recognition system. The objective of this approach is to propose an analytical offline recognition method of handwritten Arabic for rapid implementation. The first part in the writing recognition system is the preprocessing phase that prepares the data which serves to introduce and extract a set of simple statistical features by a window sliding along that text line from the right to left; then it injects the resulting feature vectors to the Hidden Markov Model Toolkit (HTK). In the recognition phase, the concatenation of characters to form words is modelled by simple lexical models; each word is modelled by a stochastic finite-state automaton (SFSA). The proposed system is applied to an "Arabic-Numbers" data corpus, which contains 47 words and 1905 sentences. These sentences are written by five different peoples.