Ancient Chinese books have great values in history and cultural studies. Named en-tities like person, location, time are cru-cial elements, thus automatic Named En-tity Recognition (NER) is considered a ba-sic task in ancient Chinese text processing. This paper introduces EvaHan2025, the first international ancient Chinese Named Entity Recognition bake-off. The evalua-tion introduces a rigorous benchmark for assessing NER performance across histori-cal and medical texts, covering 12 named entity types. A total of 13 teams par-ticipated in the competition, submitting 77 system runs. In the closed modality, where participants were restricted to us-ing only the training data, the highest F1 scores reached 85.04% on TestA and 90.28% on TestB, both derived from his-torical texts, while performance on medi-cal texts (TestC) reached 84.49%. The re-sults indicate that text genre significantly impacts model performance, with histori-cal texts generally yielding higher scores. Additionally, the intrinsic characteristics of named entities also influence recogni-tion performance. These findings high-light the challenges and opportunities in ancient Chinese NER and underscore the importance of domain adaptation and en-tity type diversity in future research.

Ancient Chinese texts have no sentence boundaries and punctuation. Adding modern Chinese punctuation to theses texts requires expertise, time and efforts. Automatic sentence segmentation and punctuation is considered as a basic task for Ancient Chinese processing, but there is no shared task to evaluate the performances of different systems. This paper presents the results of the first ancient Chinese sentence segmentation and punctuation bakeoff, which is held at the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) 2024. The contest uses metrics for detailed evaluations of 4 genres of unpublished texts with 11 punctuation types. Six teams submitted 32 running results. In the closed modality, the participants are only allowed to use the training data, the highest obtained F1 scores are respectively 88.47% and 75.29% in sentence segmentation and sentence punctuation. The perfermances on the unseen data is 10 percent lower than the published common data, which means there is still space for further improvement. The large language models outperform the traditional models, but LLM changes the original characters around 1-2%, due to over-generation. Thus, post-processing is needed to keep the text consistancy.

The digitization of ancient books necessitates the implementation of automatic word segmentation and part-of-speech tagging. However, the existing research on this topic encounters pressing issues, including suboptimal efficiency and precision, which require immediate resolution. This study employs a methodology that combines word segmentation and part-of-speech tagging. It establishes a correlation between fonts and radicals, trains the Radical2Vec radical vector representation model, and integrates it with the SikuRoBERTa word vector representation model. Finally, it connects the BiLSTM-CRF neural network.The study investigates the combination of word segmentation and part-of-speech tagging through an experimental approach using a specific data set. In the evaluation dataset, the F1 score for word segmentation is 95.75%, indicating a high level of accuracy. Similarly, the F1 score for part-of-speech tagging is 91.65%, suggesting a satisfactory performance in this task. This model enhances the efficiency and precision of the processing of ancient books, thereby facilitating the advancement of digitization efforts for ancient books and ensuring the preservation and advancement of ancient book heritage.