Open AccessNEW APPROACH FOR ONLINE ARABIC MANUSCRIPT RECOGNITION BY DEEP BELIEF NETWORK
Author(s) -
Samir Benbakreti,
Aoued Boukelif
Publication year - 2018
Publication title -
acta polytechnica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.207
H-Index - 15
eISSN - 1805-2363
pISSN - 1210-2709
DOI - 10.14311/ap.2018.58.0297
Subject(s) - arabic , artificial intelligence , computer science , character (mathematics) , character recognition , artificial neural network , natural language processing , arabic numerals , feature (linguistics) , deep learning , pattern recognition (psychology) , deep belief network , feature extraction , speech recognition , image (mathematics) , mathematics , linguistics , philosophy , geometry
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.