A large-scale dataset for Chinese historical document recognition and analysis - Yongxin Shi; Dezhi Peng; Yuyi Zhang; Jiahuan Cao; Lianwen Jin (Scientific Data)
Abstract The development of Chinese civilization has produced a vast collection of historical documents. Recognizing and analyzing these documents hold significant value for the research of ancient culture. Recently, researchers have tried to utilize deep-learning techniques to automate recognition and analysis. However, existing Chinese historical document datasets, which are heavily relied upon by deep-learning models, suffer from limited data scale, insufficient character category, and lack of book-level annotation. To fill this gap, we introduce HisDoc1B, a large-scale dataset for Chinese historical document recognition and analysis. The HisDoc1B comprises 40,281 books, over 3 million document images, and over 1 billion characters across 30,615 character categories. To the best of our knowledge, HisDoc1B is the largest dataset in the field, surpassing existing datasets by more than 200 times in scale. Additionally, it is the only dataset with book-level annotations and punctuation annotations. Furthermore, extensive experiments demonstrate the high quality and practical utility of the proposed HisDoc1B. We believe that HisDoc1B could provide valuable resources to boost the advancement of research in this domain.