Enhancing Arabic Handwritten Recognition System-Based CNN-BLSTM Using Generative Adversarial Networks

Abstract

Arabic Handwritten Recognition (AHR) presents unique challenges due to the complexity of Arabic script and the limited availability of training data. This paper proposes an approach that integrates generative adversarial networks (GANs) for data augmentation within a robust CNN-BLSTM architecture, aiming to significantly improve AHR performance. We employ a CNN-BLSTM network coupled with connectionist temporal classification (CTC) for accurate sequence modeling and recognition. To address data limitations, we incorporate a GANs based data augmentation module trained on the IFN-ENIT Arabic handwriting dataset to generate realistic and diverse synthetic samples, effectively augmenting the original training corpus. Extensive evaluations on the IFN-ENIT benchmark demonstrate the efficacy of adopted approach. We achieve a recognition rate of 95.23%, surpassing the baseline model by 3.54%. This research presents a promising approach to data augmentation in AHR and demonstrates a significant improvement in word recognition accuracy, paving the way for more robust and accurate AHR systems.