@inproceedings{yin-zhao-2026-beijing,
    title = "{B}eijing Normal University at {E}va{H}an 2026: Enhancing {A}ncient {C}hinese Character Recognition and Layout Analysis via {VLM} Fine-Tuning and Linguistic Post-Processing",
    author = "Yin, Yihuan  and
      Zhao, Qian",
    editor = "Sprugnoli, Rachele  and
      Passarotti, Marco",
    booktitle = "Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages ({LT}4{HALA} 2026) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.lt4hala-1.26/",
    doi = "10.63317/25kr52s65n9t",
    pages = "273--276",
    abstract = "This paper describes the system submitted by the Beijing Normal University (BNU) team for the EvaHan 2026 shared task. We participated in Task A (Printed Text Recognition), Task B (Layout Element Analysis), and Task C (Handwritten Character Recognition). For text recognition (Tasks A and C), we proposed a hybrid pipeline combining supervised fine-tuning (SFT) of Vision-Language Models (VLMs) with a linguistic rule-based post-processing module. In the Open Track, we further explored the use of a general-purpose VLM to correct semantic errors while maintaining visual fidelity to ancient variant characters. For Task B, we adopted a method integrating a VLM with structured prompting strategies. Our system consistently surpassed the official baselines, achieving an F1 score of 94.53{\%} in Task A and 91.33{\%} in Task C, while demonstrating enhanced localization precision in Task B."
}