Enhancing Arabic Handwritten Character Recognition Using Multi-Reservoir Spiking Neural Network

Handwritten character recognition is an important area of artificial intelligence with applications in education, digital archiving, and cultural preservation. Arabic script recognition remains particularly challenging due to its cursive structure, positional variations of letters, and reliance on diacritical marks. This study introduces a multi-reservoir Spiking Neural Network (SNN) approach that mimics biological information processing to improve recognition performance. The proposed system integrates both original and augmented (Gaussian-blurred) representations of handwritten Arabic characters, enabling the network to capture diverse handwriting variations. Experiments conducted on a dataset of 16,800 samples demonstrate that the multi-reservoir model achieves higher accuracy than a single-reservoir baseline, particularly when applied to subsets of characters. Error analysis further reveals that most misclassifications occur among visually similar characters, highlighting the intrinsic complexity of Arabic script. These findings suggest that multi-reservoir SNNs provide a promising pathway for energy-efficient, culturally relevant AI applications. Beyond technical improvement, this work contributes to the digital preservation of Arabic language resources and supports broader access to information in multilingual societies.