TrA-MHWR: Transformer-Enhanced Feature Extraction with Attention-Based Recognition for Offline Handwritten Malayalam Words

The article introduces TrA-MHWR a unique methodology transformer-based model designed to facilitate the recognition of handwritten Malayalam words in an offline setting. The model features remarkable ability to transform raw pixel data into meaningful linguistic units. Utilizing the power of Vision transformers and attention units, the model offers a sophisticated framework for robust and precise identification of handwritten Malayalam words in offline contexts. This study presents a comprehensive analysis of the architecture, training methodology, and evaluation results of TrA-MHWR, demonstrating its efficacy in pushing the boundaries related to the recognition of handwritten words in relation to Malayalam language. The proposed study uses DeiT model with a multi-head self-attention layer to improve feature sensitivity and achieved 98.82% accuracy.