EvaHan

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中文版: <a href="https://github.com/GoThereGit/EvaHan/blob/main/README_zh.md">点此跳转</a>

IMPORTANT NEWS

Registration Entry: <a href="https://jsj.top/f/nWLK2R">CLICK ME</a>

EvaHan 2026

• <a href="https://github.com/GoThereGit/EvaHan"><b>EvaHan 2026</b></a> is the Fifth International Evaluation of Ancient Chinese Information Processing, focusing on OCR tasks for multimodal large language models in ancient Chinese.

• Co-organized with LREC 2026, which will be held from May 11 to 16, 2026, in Mallorca, Spain.

• Co-organized with LT4HALA 2026, which will be held in Mallorca, Spain.

• EvaHan 2026 is organized by Dongbo Wang, Bin Li, Minxuan Feng, Chao Xu, Weiguang Qu, Liu Liu, Si Shen.

Previous Tasks

• EvaHan 2022

The First Bake-off of Ancient Chinese Automatic Processing was successfully held in Marseille, France, in 2022, with a focus on automatic word segmentation and part-of-speech tagging of ancient Chinese.

• EvaHan 2023

The Second Bake-off of Ancient Chinese Automatic Processing was successfully held in Macau, China, in 2023, with a focus on machine translation of ancient Chinese.

• <a href="https://aclanthology.org/2024.lt4hala-1.27.pdf">EvaHan 2024</a>

The Third Bake-off of Ancient Chinese Automatic Processing was held in Turin, Italy, in 2024, with a focus on automatic sentence segmentation and punctuation of ancient Chinese.

• <a href="https://aclanthology.org/2025.alp-1.19.pdf">EvaHan 2025</a>

The Fourth Bake-off of Ancient Chinese Automatic Processing was held in New Mexico, USA, in 2025, with a focus on named entity recognition in ancient Chinese.

Important Dates for EvaHan 2026(UTC+8 Beijing time)

• Registration deadline: January 30, 2026

• Training data release: January 1, 2026

• Test data release: February 3, 2026, 23:50 ~~February 1, 2026~~

• Running results submission: February 9, 2026, 23:50 ~~February 6, 2026~~

• Technical report submission deadline: February 28, 2026

• Notification of acceptance: March 2, 2026 ~~March 1, 2026~~

• Camera-ready papers due: March 10, 2026

Participation

To participate in EvaHan 2026, you must complete the following steps:

1. <a href="https://jsj.top/f/nWLK2R">Registration:</a>
   Submit a registration form to officially register your team for the task. Registration is open from December 1, 2025, to January 30, 2026. Only registered participants will gain access to the training dataset.

2. Accessing the Training Data:
   After completing the registration process, participants will receive instructions for downloading the training dataset, which includes image-text pairs from ancient Chinese texts for OCR.

3. Submitting Results and Reports:
   Participants must use the provided test data to generate results and submit their system outputs and a technical report as per the shared task schedule.

For inquiries or to request the registration form, please contact us at evahan2026@gmail.com.

Task

This section offers a detailed description of the tasks encompassed in EvaHan 2026.

Ancient literature, a precious heritage of Chinese civilization, exists primarily in handwritten forms or archaic printed fonts. While diverse in preservation formats, these materials are extremely fragile. Optical Character Recognition (OCR) technology enables the transformation of these paper-based or imaged ancient books into editable digital text, facilitating efficient retrieval, analysis, and dissemination. The application of OCR in this field will significantly enhance the efficiency of literature utilization and promote the digital preservation of cultural heritage. Furthermore, it provides scholars with convenient research tools, fosters the popularization and innovation of knowledge contained in ancient books, and advances the development of the humanities and social sciences.

Task A: Printed Text Recognition Ancient printed fonts present typical challenges such as variant characters, complex layouts, missing characters, and stains, making recognition significantly more difficult than modern printed text. Task A employs Character Error Rate (CER, main metric), F1-score (character/word level), and Normalized Edit Distance (NED) as the evaluation metrics. These metrics comprehensively measure model performance regarding character accuracy, sequence integrity, and edit cost. The task aims to accelerate the digitization of ancient books and improve the practical precision and robustness of automated transcription for printed ancient literature.

Task B: Layout Element Analysis Task B focuses on Layout Element Recognition. The core objective is the precise identification of four key elements within the pages of ancient books: text, image, book_edge, and seal. This task selects mAP (mean Average Precision, main metric), IoU (Intersection over Union), and F1-score as evaluation metrics. These metrics scientifically quantify the model’s recognition effectiveness across multiple dimensions, including detection accuracy, regional matching degree, and comprehensive recognition performance.

Task C: Handwritten Character Recognition The styles of handwritten ancient books are highly personalized and present multiple challenges, including challenges such as cursive connections, variant character forms, stroke omissions, and traces of corrections. Task C adopts Character Error Rate (CER, main metric), F1-score, and Normalized Edit Distance (NED) as evaluation metrics to comprehensively assess model performance in terms of character accuracy, sequence consistency, and edit distance. This task aims to break through technical bottlenecks in the automated transcription of handwritten ancient texts, providing critical support for the digital preservation and in-depth utilization of precious ancient manuscripts.

Data

The Evahan 2026 dataset comprises three datasets, covering image-text pairs: plain text images, mixed image-text images, and handwritten images-text. The data underwent initial automatic annotation, followed by meticulous correction and refinement by experts in classical Chinese language and history to ensure the highest quality of the training materials and gold-standard texts.

● Dataset A (Printed Texts) consists of data selected from the Siku Quanshu (Complete Library of the Four Treasuries), including classics, history, philosophy, and literature, as well as various other ancient books.

● Dataset B (Mixed Layouts) contains mixed image-text data selected from the Siku Quanshu and other ancient books.

● Dataset C (Handwritten Texts) includes handwritten ancient books, primarily the Chinese Buddhist canon, including the Chinese Buddhist canon (TKH) dataset, and the Chinese Buddhist canon (MTH) dataset.

Data Format

All data is presented in image-text pairs and stored in JSON files with multiple encoding formats. The specific format is shown in Table 1.

Table 1. Examples of Ancient Chinese OCR Corpus

| Picture | Text | |---------|------| | <img src="img/image2.png" alt="1761273613524" width="192"> Printed Texts | 欽定四庫全書 史部十一\n 三呉水考 地理類四{{河渠之屬/}}\n 提要\n {{臣/}}等謹案三呉水考十六卷明張内藴周大\n 韶仝撰内藴稱呉江生員大韶稱華亭監生\n 其始末則均未詳也初萬厯四年言官論蘇\n 松常鎮諸府水利久湮宜及時修濬乞遣御\n 史一員督其事乃命御史懷安林應訓往應 | | <img src="img/image10.png" alt="descript" width="233"> Mixed Layouts | {"label": "book_edge", "points": [[2, 14], [17, 14], [17, 655], [2, 655]]}, {"label": "image", "points": [[28, 107], [97, 107], [97, 297], [28, 297]]}, {"label": "image", "points": [[119, 124], [198, 124], [198, 351], [119, 351]]}, {"label": "text", "points": [[219, 60], [254, 60], [254, 171], [219, 171]]}, {"label": "text", "points": [[40, 444], [74, 444], [74, 551], [40, 551]]}, {"label": "text", "points": [[137, 441], [176, 441], [176, 548], [137, 548]]}, {"label": "text", "points": [[276, 23], [321, 23], [321, 137], [276, 137]]}, {"label": "text", "points": [[336, 26], [381, 26], [381, 767], [336, 767]]}, {"label": "image", "points": [[413, 113], [492, 113], [492, 307], [413, 307]]}, {"label": "text", "points": [[430, 442], [472, 442], [472, 518], [430, 518]]} | | <img src="img/image4.png" alt="" width="293"> Handwritten Texts | 言卽眼識界若有爲若無爲增語是\n 菩薩摩訶薩卽耳鼻舌身意識界若\n 有爲若無爲增語是菩薩摩訶薩善\n 現汝復觀何義言卽眼識界若有漏\n 若無漏增語非菩薩摩訶薩卽耳鼻\n 舌身意識界若有漏若無漏增語非\n 菩薩摩訶薩耶世尊若眼識界有漏\n 無漏若耳鼻舌身意識界有漏無漏\n 尚畢竟不可得性非有故況有眼識\n 界有漏無漏增語及耳鼻舌身意識\n 界有漏無漏增語此增語旣非有如\n 何可言卽眼識界若有漏若無漏增\n 語是菩薩摩訶薩卽耳鼻舌身意識\n 界若有漏若無漏增語是菩薩摩訶\n 鼻...... |

Training Data The training set consists of designated portions of subsets A, B, and C. All training samples are provided in image-text pair format, with text in Traditional Chinese, approximately 5000 image-text pairs per subset. Registered participants will receive the training data via email.

Test Data The test set includes the remaining unseen portions of subsets A, B, and C to ensure comprehensive evaluation of all three challenge types. The data is also provided in image-text pair format, approximately 200-500 image-text pairs per subset. Detailed information and a download link for the test data will be provided to participants before the start of the formal evaluation period.

Metrics

Prior to the official competition commencement, each participating team may only access the training data. Subsequently, the unlabeled test data will be released on February 3, 2026. Following the completion of evaluations, the labels for the test data will also be published. Tables 2 and 3 provide examples of the scorers' outputs.

Table 2. Character-Level Recognition Performance of the OCR Module

| Task | Precision| Recall | F1_Score | CER | NED | |:---:|:---:|:---:|:---:|:---:|:---:|