@inproceedings{cheng-etal-2026-ancient,
title = "Ancient-Bench: A Comprehensive Multi-millennial, Multi-medium, and Multi-script Benchmark for {A}ncient {C}hinese Artifact Text Recognition",
author = "Cheng, Hiuyi and
Xu, Nuo and
Zhang, Yuyi and
Zheng, Xuhan and
Pan, Wei and
Zhang, Jing and
Peng, Dezhi and
Liao, Minghui and
Yihua, Teng and
Wu, Jihao and
Ren, Haoyu and
Jin, Lianwen",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1758/",
pages = "37595--37626",
ISBN = "979-8-89176-462-0",
abstract = "Ancient Chinese artifact recognition is fundamental to heritage digitization, and benchmarks for ancient texts are essential for evaluating current model capabilities. However, existing benchmarks suffer from ``fragmentation'', manifested in limited temporal coverage, limited medium diversity, and incomplete script types. Therefore, we present Ancient-Bench, a comprehensive benchmark of 2,700 images for ancient Chinese artifact text recognition, featuring three dimensions: Multi-millennial (spanning 3,000 years of character evolution), Multi-medium (covering nine artifact categories), and Multi-script (encompassing seven historical script forms). To enable consistent and fair evaluation across heterogeneous media, we further define three annotation standards tailored to the medium-specific characteristics of ancient texts: symbol standardization, character standardization, and parsing standardization. Extensive experiments on Ancient-Bench covering general Vision-Language Models (VLMs) and OCR-specialist models reveal that ancient Chinese recognition remains fundamentally unsolved, with persistent challenges in variant characters, specialized symbols, and hallucination. The data and evaluation code will be publicly available."
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%0 Conference Proceedings
%T Ancient-Bench: A Comprehensive Multi-millennial, Multi-medium, and Multi-script Benchmark for Ancient Chinese Artifact Text Recognition
%A Cheng, Hiuyi
%A Xu, Nuo
%A Zhang, Yuyi
%A Zheng, Xuhan
%A Pan, Wei
%A Zhang, Jing
%A Peng, Dezhi
%A Liao, Minghui
%A Yihua, Teng
%A Wu, Jihao
%A Ren, Haoyu
%A Jin, Lianwen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F cheng-etal-2026-ancient
%X Ancient Chinese artifact recognition is fundamental to heritage digitization, and benchmarks for ancient texts are essential for evaluating current model capabilities. However, existing benchmarks suffer from “fragmentation”, manifested in limited temporal coverage, limited medium diversity, and incomplete script types. Therefore, we present Ancient-Bench, a comprehensive benchmark of 2,700 images for ancient Chinese artifact text recognition, featuring three dimensions: Multi-millennial (spanning 3,000 years of character evolution), Multi-medium (covering nine artifact categories), and Multi-script (encompassing seven historical script forms). To enable consistent and fair evaluation across heterogeneous media, we further define three annotation standards tailored to the medium-specific characteristics of ancient texts: symbol standardization, character standardization, and parsing standardization. Extensive experiments on Ancient-Bench covering general Vision-Language Models (VLMs) and OCR-specialist models reveal that ancient Chinese recognition remains fundamentally unsolved, with persistent challenges in variant characters, specialized symbols, and hallucination. The data and evaluation code will be publicly available.
%U https://aclanthology.org/2026.findings-emnlp.1758/
%P 37595-37626
Markdown (Informal)
[Ancient-Bench: A Comprehensive Multi-millennial, Multi-medium, and Multi-script Benchmark for Ancient Chinese Artifact Text Recognition](https://aclanthology.org/2026.findings-emnlp.1758/) (Cheng et al., Findings 2026)
ACL
- Hiuyi Cheng, Nuo Xu, Yuyi Zhang, Xuhan Zheng, Wei Pan, Jing Zhang, Dezhi Peng, Minghui Liao, Teng Yihua, Jihao Wu, Haoyu Ren, and Lianwen Jin. 2026. Ancient-Bench: A Comprehensive Multi-millennial, Multi-medium, and Multi-script Benchmark for Ancient Chinese Artifact Text Recognition. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 37595–37626, Budapest, Hungary. Association for Computational Linguistics.