Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition

Rahal, Najoua; Tounsi, Maroua; Hussain, Amir; Alimi, Adel M.

Abstract

One of the most recent challenging issues of pattern recognition and artificial intelligence is Arabic text recognition. This research topic is still a pervasive and unaddressed research field, because of several factors. Complications arise due to the cursive nature of the Arabic writing, character similarities, unlimited vocabulary, use of multi-size and mixed-fonts, etc. To handle these challenges, an automatic Arabic text recognition requires building a robust system by computing discriminative features and applying a rigorous classifier together to achieve an improved performance. In this work, we introduce a new deep learning based system that recognizes Arabic text contained in images. We propose a novel hybrid network, combining a Bag-of-Feature (BoF) framework for feature extraction based on a deep Sparse Auto-Encoder (SAE), and Hidden Markov Models (HMMs), for sequence recognition. Our proposed system, termed BoF-deep SAE-HMM, is tested on four datasets, namely the printed Arabic line images Printed KHATT (P-KHATT), the benchmark printed word images Arabic Printed Text Image (APTI), the benchmark handwritten Arabic word images IFN/ENIT, and the benchmark handwritten digits images Modified National Institute of Standards and Technology (MNIST).

Citation

Rahal, N., Tounsi, M., Hussain, A., & Alimi, A. M. (2021). Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition. IEEE Access, 9, 18569-18584. https://doi.org/10.1109/access.2021.3053618

• Journal Article Type: Article
• Acceptance Date: Jan 12, 2021
• Online Publication Date: Jan 22, 2021
• Publication Date: 2021
• Deposit Date: Feb 22, 2021
• Publicly Available Date: Feb 22, 2021
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Pages: 18569-18584
• DOI: https://doi.org/10.1109/access.2021.3053618
• Keywords: Arabic text recognition, feature learning, bag of features, sparse auto-encoder, hidden Markov models
• Public URL: http://researchrepository.napier.ac.uk/Output/2745560