Title: Hybrid modelling of an off line Arabic handwriting recognition system: results and evaluation
Authors: Ons Meddeb; Mohsen Maraoui; Shadi Aljawarneh
Addresses: Computational Mathematics Laboratory, University of Monastir, Monastir, Tunisia ' Computational Mathematics Laboratory, University of Monastir, Monastir, Tunisia ' Software Engineering Department, CIT Faculty, Jordan University of Science and Technology, Jordan
Abstract: In this paper, we presented a state of the art in the field of Arabic handwriting recognition as well as the techniques used. Then, we detailed the general architecture of an Arabic Handwriting Recognition System (AHRS) and the contributions that we have proposed at each phase: first, an analytical segmentation approach based on a morphological/structural analysis of the entire word and sub word; then, a combination of different features to extract the relevant information from text image; thereafter, a hybrid classification approach using long short term memory recurrent neural network (LSTM-RNN) and different other statistical classifiers to learn many characters shapes simultaneously and to improve the performance of our proposed system; and finally, a post-processing approach to reconstruct the characters, words and lines of the text using Morpho-Syntactic analyser 'AlKhalil Morph Sys'. Our proposed system has shown promising results to solve the difficulties of Arabic handwriting recognition.
Keywords: handwriting recognition; off line handwriting; Arabic handwriting recognition system; AHRS; hybrid model; long short term memory recurrent neural network; LSTM-RNN; natural language processing; NLP; pre-processing; segmentation; features extraction; classification; post-processing; Arabic language.
International Journal of Intelligent Enterprise, 2017 Vol.4 No.1/2, pp.168 - 189
Available online: 29 Sep 2017 *Full-text access for editors Access for subscribers Free access Comment on this article