Improved HMM for Cursive Arabic Handwriing Recognition System using MLP Classifier


  • Mouhcine Rabi Laboratory IRF-SIC, faculty of sciences IbnZohr University, Agadir Morocco
  • Mustapha Amrouch Laboratory IRF-SIC, faculty of sciences IbnZohr University, Agadir Morocco
  • Zouhir Mahani High school of technology, IbnZohr University, Agadir Morocco



Arabic Handwriting Recognition, Context, Embedded training, HMMs, Multilayer Perceptron (MLP).


Recognizing unconstrained cursive Arabic handwritten text is a very challenging task the use of hybrid classification to take advantage of the strong modeling of Hidden Markov Models (HMM) and the large capacity of discrimination related to Multilayer Perceptron (MLP) is a very important component in recognition systems.The proposed work reports an effective method on improvement our previous work that takes into consideration the context of character by applying an embedded training based HMMs this HMM is enhanced by an Artificial neural network that are incorporated into the process of classification to estimate the emission probabilities. The experiments are done on the same benchmark IFN/ENIT database of our previous work to compare the results and show the effectiveness of hybrid classifier for enhancing the recognition rate the results are promising and encouraging.


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How to Cite

Rabi, M., Amrouch, M., & Mahani, Z. (2017). Improved HMM for Cursive Arabic Handwriing Recognition System using MLP Classifier. Transactions on Machine Learning and Artificial Intelligence, 5(4).



Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems