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arXiv · 2508.21098

TrInk: Ink Generation with Transformer Network

Zezhong Jin·Shubhang Desai·Xu Chen·Biyi Fang·Zhuoyi Huang·Zhe Li·Chong-Xin Gan·Xiao Tu·Man-Wai Mak·Yan Lu·Shujie Liu
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Abstract

In this paper, we propose TrInk, a Transformer-based model for ink generation, which effectively captures global dependencies. To better facilitate the alignment between the input text and generated stroke points, we introduce scaled positional embeddings and a Gaussian memory mask in the cross-attention module. Additionally, we design both subjective and objective evaluation pipelines to comprehensively assess the legibility and style consistency of the generated handwriting. Experiments demonstrate that our Transformer-based model achieves a 35.56\% reduction in character error rate (CER) and an 29.66% reduction in word error rate (WER) on the IAM-OnDB dataset compared to previous methods. We provide an demo page with handwriting samples from TrInk and baseline models at: https://akahello-a11y.github.io/trink-demo/

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Zezhong Jin, Shubhang Desai, Xu Chen, Biyi Fang, Zhuoyi Huang, Zhe Li, Chong-Xin Gan, Xiao Tu, Man-Wai Mak, Yan Lu, Shujie Liu. 2025-08-28. TrInk: Ink Generation with Transformer Network. https://arxiv.org/abs/2508.21098

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