Shui manuscripts provide a historical testimony of the national identity and spirit of the Shui people. In response to the lack of a high-quality Shui manuscripts dataset, we collected Shui manuscript images in the Shui area and used various methods to enhance them. Through our efforts, we created a well-labeled and sizable Shui manuscripts dataset, named Shuishu_T, which is the largest of its kind. Then, we applied target detection technology for Shui manuscript characters recognition. Specifically, we compared the advantages and disadvantages of Faster R-CNN, you only look once (YOLO), and single shot multibox detector (SSD), and subsequently chose Faster R-CNN to detect and recognize Shui manuscript characters. We trained and tested 111 classes of Shui manuscript characters with Faster R-CNN and achieved an average recognition rate of 87.8%. Finally, we designed a WeChat applet that can be used to quickly identify Shui manuscript characters in images obtained by scanning Shui manuscripts with a mobile phone. This work provides a basis for realizing the recognition of characters in Shui manuscripts on mobile terminals. Our research enables the intangible cultural heritage of the Shui people to be preserved, promoted, and shared, which is of great significance for the conservation and inheritance of Shui manuscripts.

Shui manuscripts are part of the national intangible cultural heritage of China. Owing to the particularity of text reading, the level of informatization and intelligence in the protection of Shui manuscript culture is not adequate. To address this issue, this study created Shuishu_C, the largest image dataset of Shui manuscript characters that has been reported. Furthermore, after extensive experimental validation, we proposed ShuiNet-A, a lightweight artificial neural network model based on the attention mechanism, which combines channel and spatial dimensions to extract key features and finally recognize Shui manuscript characters. The effectiveness and stability of ShuiNet-A were verified through multiple sets of experiments. Our results showed that, on the Shui manuscript dataset with 113 categories, the accuracy of ShuiNet-A was 99.8%, which is 1.5% higher than those of similar studies. The proposed model could contribute to the classification accuracy and protection of ancient Shui manuscript characters.