@inproceedings{jin-etal-2025-trink,
title = "{T}r{I}nk: Ink Generation with Transformer Network",
author = "Jin, Zezhong and
Desai, Shubhang and
Chen, Xu and
Fang, Biyi and
Huang, Zhuoyi and
Li, Zhe and
Gan, Chong-Xin and
Tu, Xiao and
Mak, Man-Wai and
Lu, Yan and
Liu, Shujie",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.244/",
pages = "4857--4864",
ISBN = "979-8-89176-332-6",
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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<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/</abstract>
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%0 Conference Proceedings
%T TrInk: Ink Generation with Transformer Network
%A Jin, Zezhong
%A Desai, Shubhang
%A Chen, Xu
%A Fang, Biyi
%A Huang, Zhuoyi
%A Li, Zhe
%A Gan, Chong-Xin
%A Tu, Xiao
%A Mak, Man-Wai
%A Lu, Yan
%A Liu, Shujie
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F jin-etal-2025-trink
%X 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/
%U https://aclanthology.org/2025.emnlp-main.244/
%P 4857-4864
Markdown (Informal)
[TrInk: Ink Generation with Transformer Network](https://aclanthology.org/2025.emnlp-main.244/) (Jin et al., EMNLP 2025)
ACL
- Zezhong Jin, Shubhang Desai, Xu Chen, Biyi Fang, Zhuoyi Huang, Zhe Li, Chong-Xin Gan, Xiao Tu, Man-Wai Mak, Yan Lu, and Shujie Liu. 2025. TrInk: Ink Generation with Transformer Network. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 4857–4864, Suzhou, China. Association for Computational Linguistics.