AMRC-NET: A New Method for Recognition of Russian Handwritten Text Integrating Multipath Mechanism and Linguistic Features

Russian handwritten text recognition presents significant challenges due to the complex morphology of the Cyrillic alphabet, prevalent cursive writing, and substantial writer variability. To address the limitations of existing methods in dynamic contextual modeling and language-specific feature adaptation, this paper proposes an end-to-end framework named AMRC-NET. This framework integrates a multi-path architecture with linguistic feature awareness through three core modules: a Context Enhancement Module for long-range dependency modeling, a Russian Alphabet Morphology Optimization Module for script-specific pattern capture, and a Multi-Path Adaptive Fusion Mechanism for dynamic output integration. Extensive experiments on the Cyrillic Handwriting Dataset demonstrate that AMRC-NET achieves 48.15% in recognition accuracy, significantly outperforming existing models such as STAR-Net and GRCNN. Ablation studies further reveal that the three modules contribute accuracy improvements of 4.82%, 2.03%, and 1.58%, respectively, confirming their individual effectiveness and synergistic integration. The model also exhibits enhanced generalization capability, as reflected by lower validation loss and a reduced overfitting gap, while visual analysis confirms its robustness in challenging cursive writing scenarios.