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2018 | OriginalPaper | Chapter

Convolutional Feature Learning and CNN Based HMM for Arabic Handwriting Recognition

Authors : Mustapha Amrouch, Mouhcine Rabi, Youssef Es-Saady

Published in: Image and Signal Processing

Publisher: Springer International Publishing

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Abstract

In this paper, we present a model CNN based HMM for Arabic handwriting word recognition. The HMM have proved a powerful to model the dynamics of handwriting. Meanwhile, the CNN have achieved impressive performance in many computer vision tasks, including handwritten characters recognition. In this model, the trainable classifier of CNN is replacing by the HMM classifier. CNN works as a generic feature extractor and HMM performs as a recognizer. The suggested system outperforms a basic HMM based on handcrafted features. Experiments have been conducted on the well-known IFN/ENIT database. The results obtained show the robustness of the proposed approach.

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Metadata
Title
Convolutional Feature Learning and CNN Based HMM for Arabic Handwriting Recognition
Authors
Mustapha Amrouch
Mouhcine Rabi
Youssef Es-Saady
Copyright Year
2018
Book
Image and Signal Processing

Print ISBN: 978-3-319-94210-0

Electronic ISBN: 978-3-319-94211-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-94211-7_29

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