<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yin-zhao-2026-beijing">
    <titleInfo>
        <title>Beijing Normal University at EvaHan 2026: Enhancing Ancient Chinese Character Recognition and Layout Analysis via VLM Fine-Tuning and Linguistic Post-Processing</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yihuan</namePart>
        <namePart type="family">Yin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Qian</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Rachele</namePart>
            <namePart type="family">Sprugnoli</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marco</namePart>
            <namePart type="family">Passarotti</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA Language Resources Association (ELRA)</publisher>
            <place>
                <placeTerm type="text">Palma, Mallorca (Spain)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">yin-zhao-2026-beijing</identifier>
    <identifier type="doi">10.63317/25kr52s65n9t</identifier>
    <location>
        <url>https://aclanthology.org/2026.lt4hala-1.26/</url>
    </location>
    <part>
        <date>2026-05</date>
        <extent unit="page">
            <start>273</start>
            <end>276</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Beijing Normal University at EvaHan 2026: Enhancing Ancient Chinese Character Recognition and Layout Analysis via VLM Fine-Tuning and Linguistic Post-Processing
%A Yin, Yihuan
%A Zhao, Qian
%Y Sprugnoli, Rachele
%Y Passarotti, Marco
%S Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F yin-zhao-2026-beijing
%X 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.
%R 10.63317/25kr52s65n9t
%U https://aclanthology.org/2026.lt4hala-1.26/
%U https://doi.org/10.63317/25kr52s65n9t
%P 273-276

Markdown (Informal)

[Beijing Normal University at EvaHan 2026: Enhancing Ancient Chinese Character Recognition and Layout Analysis via VLM Fine-Tuning and Linguistic Post-Processing](https://aclanthology.org/2026.lt4hala-1.26/) (Yin & Zhao, LT4HALA 2026)

• Beijing Normal University at EvaHan 2026: Enhancing Ancient Chinese Character Recognition and Layout Analysis via VLM Fine-Tuning and Linguistic Post-Processing (Yin & Zhao, LT4HALA 2026)

ACL

• Yihuan Yin and Qian Zhao. 2026. Beijing Normal University at EvaHan 2026: Enhancing Ancient Chinese Character Recognition and Layout Analysis via VLM Fine-Tuning and Linguistic Post-Processing. In Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026, pages 273–276, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).