Application of computer vision-based dynamic stroke analysis in intelligent calligraphy teaching systems

This study introduces a novel temporal-spatial CNN-LSTM architecture that uniquely integrates real-time stroke tracking with dynamic execution analysis for intelligent calligraphy instruction, achieving 67 ms processing latency for immediate feedback delivery. Randomized controlled experiments with 40 participants across four skill levels demonstrated significant educational superiority over traditional methods: 15.2% higher stroke accuracy, 46.4% faster learning rates, and 7.7% better retention (all p <0.05), with 94.7% system recognition accuracy. This technology enables scalable, objective calligraphy assessment while preserving cultural authenticity, offering transformative potential for traditional arts education through real-time multimodal feedback mechanisms.