## 2 Related Work  
Optical character recognition technology is now quite mature and has been utilized for the detection and recognition of various mainstream languages in many fields, such as ticket recognition, intelligent medical procedures, and license plate recognition. However, character recognition of many endangered minority ancient manuscripts has not received sufficient attention. In recent years, various studies on text detection and recognition in some minority languages have been conducted, mostly using deep neural network-based detection methods. For example, [PERSON] and [PERSON] [1] identified ancient Geez characters in Ethiopia and achieved 99.39% accuracy by using deep convolutional neural networks. [PERSON] et al. [2] identified ancient Vietnamese manuscripts and proposed a learning-based alignment method combined with YOLOv5m for detection and recognition; they achieved an accuracy of 96.4% without manual annotation. [PERSON] and [PERSON] [3] identified characters in images of Tulu palm leaf manuscripts and achieved a recognition rate of 79.92% using a deep convolutional neural network. [PERSON] et al. [4] identified Persian characters and achieved an average accuracy of 99.69% on a dataset of five million Persian character images; they had the advantage of having access to an astonishing number of data samples. [PERSON] et al. [5] performed recognition of handwritten Tamil characters and proposed a modified convolutional neural network that recognized the characters with 97.07% accuracy on an experimental dataset.  
[PERSON] and [PERSON] [6] proposed a classification method combining computational geometry algorithms and convolutional neural networks for the recognition of handwritten Arabic characters. [PERSON] et al. [7] identified ancient Yi characters in China and proposed a detection method based on connected components and regression character segmentation, which solved the problem of character detection and segmentation of ancient Yi characters. [PERSON] et al. [8] identified oracle bones, the earliest text in China, and proposed using the target detection algorithm faster region-based convolutional neural network (Faster R-CNN) [9] combined with multi-scale fusion features to improve the text detection ability. They achieved an average accuracy rate of 88.2%. [PERSON] et al. [10] designed a Braille character detection system with an opportunistic deep neural network, which contributed to helping visually impaired people learn computer programming.  
Only a few studies have been published on Shui manuscript characters recognition. Most studies are predominantly on Shui manuscript characters classification or characters segmentation. [PERSON] and [PERSON] [11] proposed a four-layer CNN that classified 50,000 Shui manuscript character images and achieved a classification accuracy of 93.3%. [PERSON] [12] proposed an 11-layer CNN for Shui manuscript characters recognition and classified 60,000 Shui manuscript character images (including 80 categories). A classification accuracy of 98.3% was achieved. [PERSON] et al. [13] proposed a model that used the feedback of a CNN to determine the hyperparameters for clustering labels. Using 6,230 Shui manuscript character images in experiments, they demonstrated that the model which they proposed can be utilized for ancient text recognition. However, there were many errors in the text and automatic annotation. [PERSON] [14] input a total of 8,500 Shui manuscript character samples from 17 categories into a CNN with only one layer and achieved a classification accuracy of 93.74%. [PERSON] et al. [15] proposed a method based on adaptive image enhancement, region detection, and segmentation, and applied it to the segmentation of Shui manuscript character images. They achieved a good character segmentation effect.