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arXiv Computer Vision By Ranjit Raut, Aarav Subedi, Ashim Shrestha

Rethinking Handwritten Character Recognition

Read the original on arXiv Computer Vision →

The paper introduces GraphemeNet, a unified multi‑script handwritten character recognition architecture that explicitly encodes script‑geometric regularities. It uses two orthogonal binary axes: Persistent Scaffold Injection (PSI) to embed stroke‑level geometry into each encoder stage, and a choice between gated global pooling or a Stroke Topology Module for spatial relational reasoning. Across fourteen benchmarks in eight writing systems, GraphemeNet achieves state‑of‑the‑art performance with fewer parameters, demonstrating the effectiveness of structural‑prior efficiency for multi‑script HCR.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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