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 inte... More >