Abstract
A new hybrid system of off-line analytical recognition of Arabic handwriting combining a neural network type multi-layer perceptron (MLP) and hidden Markov models (HMM) is presented. We propose a way to cooperate HMM and MLP neural network in a probabilistic architecture taking advantage of both tools dedicated to the recognition of Arabic literal amounts. This description is based on statistical and structural characteristics extraction of the significant character of the handwritten Arabic words, which can be used in the MLP classification module to estimate probabilities used as the observations to perform a recognition by the HMM. The originality of our approach is based on the segmentation into characters taking into account diacritics with the characters that match them. The experiments show the convergence of the global system, even with a random initialization of the neural network.Keywords - Recognition of Arabic handwriting, hidden Markov models, fast K-means, Arabic literal amounts, multi-layer perceptron. * E-mail: khaoula_1190@hotmail.com
Highlights
The handwriting recognition is in the domain of the pattern recognition which is interested in the forms of characters
We have proposed a recognition system evaluated using a hybrid model-based on neural network type multi-layer perceptron and hidden Markov models in a limited vocabulary
Our goal is off-line recognition of handwritten Arabic words with a limited vocabulary using an analytical method with application on the Arabic literal amounts
Summary
The handwriting recognition is in the domain of the pattern recognition which is interested in the forms of characters. Automatic recognition of writing consists of the creation of systems capable of recognizing handwritten or printed characters. The majority of proposed solutions were tested on Latin writing and applied as such for the recognition of printed Arabic script. These methods generally assume that characters can be isolated by a segmentation step. This segmentation step is possible in the case of a printed Latin text, but very difficult in the case of cursive or semi-cursive Arabic writing. More research is needed in all the recognition system stages especially the segmentation and the classification stages, since they are the most challenging tasks in the off-line Arabic handwritten recognition system
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
