Researchers Propose GraphemeNet for Handwritten Character Recognition

In a research preprint published on arXiv, scientists introduced GraphemeNet, a unified multi-script architecture tailored for non-Latin handwritten character recognition (HCR). The work focuses on structural-prior efficiency, encoding stroke-geometric regularities directly into the model.

The framework incorporates Persistent Scaffold Injection (PSI) to anchor learned features to script geometry, alongside a Stroke Topology Module (STM) and Linear Capsule Routing (LCR) operating with O(n) complexity. This design allows the network to adapt across different scripts with minimal structural adjustments.

Evaluated on fourteen benchmarks spanning eight writing systems, GraphemeNet consistently outperformed existing baselines while reducing parameter requirements, confirming the efficacy of structural priors in multi-script character recognition.