Exploring Deep Learning Approaches to Recognize Handwritten Arabic Texts

• Published in: IEEE access 2020, Vol.8, p.89882-89898
• Main Authors: Eltay, Mohamed, Zidouri, Abdelmalek, Ahmad, Irfan
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Arabic handwriting recognition (AHR); bi-directional long short-term memory (BLSTM); connectionist temporal classification (CTC); convolutional neural networks (CNN); Deep learning; deep learning neural network (DLNN); Feature extraction; Handwriting recognition; Hidden Markov models; IFN/ENIT database; Image segmentation; long short-term memory (LSTM); Machine learning; recurrent neural network (RNN); Shape; Text recognition; Weight; word beam search (WBS)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2994248

Recognition of cursive handwritten Arabic text is a difficult problem because of context-sensitive character shapes, the non-uniform spacing between words and within a word, diverse placements of dots, and diacritics, and very low inter-class variation among individual classes. In this paper, we review and investigate different deep learning architectures and modeling choices for Arabic handwriting recognition. Further, we address the problem that imbalanced data sets present to deep learning systems. In order to address this issue, we are presenting a novel adaptive data-augmentation algorithm to promote class diversity. This algorithm assigns a weight to each word in the database lexicon. This weight is calculated based on the average probability of each class in a word. Experimental results on the IFN/ENIT and AHDB databases have shown that our presented approach yields state-of-the-art results.