@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://preview.aclanthology.org/ingest-emnlp/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/"
}