NEW APPROACH FOR ONLINE ARABIC MANUSCRIPT RECOGNITION BY DEEP BELIEF NETWORK

Authors

  • Benbakreti Samir University of Djillali Liabes Departement of Electronique, Sidi Bel Abbes 22000, Algeria http://orcid.org/0000-0001-6587-6626
  • Aoued Boukelif University of Djillali Liabes Departement of Electronique, Sidi Bel Abbes 22000, Algeria

DOI:

https://doi.org/10.14311/AP.2018.58.0297

Keywords:

manuscript, online recognition, neural networks, MLP, TDNN, RBF, deep learning, DBN.

Abstract

In this paper, we present a neural approach for an unconstrained Arabic manuscript recognition using the online writing signal rather than images. First, we build the database which contains 2800 characters and 4800 words collected from 20 different handwritings. Thereafter, we will perform the pretreatment, feature extraction and classification phases, respectively. The use of a classical neural network methods has been beneficial for the character recognition, but revealed some limitations for the recognition rate of Arabic words. To remedy this, we used a deep learning through the Deep Belief Network (DBN) that resulted in a 97.08% success rate of recognition for Arabic words.

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Published

2018-10-31

How to Cite

Samir, B., & Boukelif, A. (2018). NEW APPROACH FOR ONLINE ARABIC MANUSCRIPT RECOGNITION BY DEEP BELIEF NETWORK. Acta Polytechnica, 58(5), 297–307. https://doi.org/10.14311/AP.2018.58.0297

Issue

Section

Articles