Hybrid Neutrosophic Deep Learning Model for Enhanced Arabic Handwriting Recognition

Recognizing handwritten Arabic characters poses a significant challenge due to the complexities of the cursive script and the visual similarities between characters. While deep learning techniques have shown substantial promise, advancements in model architectures are essential to further enhance performance. Neutrosophic Sets (NS) have demonstrated their potential in improving classification models by effectively handling indeterminate and inconsistent data. This paper introduces a novel approach that integrates Neutrosophic Sets with a hybrid deep learning model, combining Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (Bi-LSTM and Bi-GRU). This integration allows for the extraction of spatial features and modeling of temporal dynamics in handwritten Arabic text. Experiments conducted on the Hijjaa and AHCD datasets revealed that the NS_CNN_Bi-LSTM model achieved an accuracy of 92.38% on the Hijjaa dataset, while the NS_CNN_Bi-GRU model attained 97.38% accuracy on the AHCD dataset, outperforming previous deep learning approaches. These results highlight the significant performance improvements achieved through advanced temporal modeling and contextual representation, without the need for explicit segmentation. The findings contribute to the ongoing development of highly accurate and sophisticated deep learning systems for Arabic handwriting recognition, with broad applications in areas requiring efficient extraction of text from handwritten documents.