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Hybrid HMM/MLP Models for Recognizing Unconstrained Cursive Arabic Handwritten Text

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Advanced Information Technology, Services and Systems (AIT2S 2017)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 25))

Abstract

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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Author information

Authors and Affiliations

  1. Laboratory IRF-SIC, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco

    Mouhcine Rabi & Mustapha Amrouch

  2. High School of Technology, Ibn Zohr University, Agadir, Morocco

    Zouhir Mahani

Authors
  1. Mouhcine Rabi
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  2. Mustapha Amrouch
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  3. Zouhir Mahani
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Corresponding author

Correspondence to Mouhcine Rabi .

Editor information

Editors and Affiliations

  1. Faculty of Sciences and Technologies, Tangier, Morocco

    Mostafa Ezziyyani

  2. Faculty of Sciences and Technologies, University Hassan 1st, Settat, Morocco

    Mohamed Bahaj

  3. Faculty of Sciences and Technologies, Mohammedia, Morocco

    Faddoul Khoukhi

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Rabi, M., Amrouch, M., Mahani, Z. (2018). Hybrid HMM/MLP Models for Recognizing Unconstrained Cursive Arabic Handwritten Text. In: Ezziyyani, M., Bahaj, M., Khoukhi, F. (eds) Advanced Information Technology, Services and Systems. AIT2S 2017. Lecture Notes in Networks and Systems, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-69137-4_39

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  • DOI: https://doi.org/10.1007/978-3-319-69137-4_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69136-7

  • Online ISBN: 978-3-319-69137-4

  • eBook Packages: EngineeringEngineering (R0)

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