## 2 Related Work  
Research on the identification of Tibetan documents began in the 1980s when [PERSON] at Tohoku University of Technology in Japan began his research on the identification of woodcut Tibetan documents in 1991. A method based on logical inference is proposed for slicing and recognizing characters in documents [8], and research is carried out on feature extraction [9] and recognition of similar characters in documents [10]. In 2011, [PERSON] at the University of California, Berkeley, proposed a complete scheme for recognizing engraved Tibetan texts, addressing the baseline detection, line character slicing and various problems faced in the recognition process [11]. Since 2017, [PERSON]'s team from Northwest University for Nationalities and [PERSON]'s team from Beijing University of Technology have been studying the recognition of Tibetan characters in Tibetan historical documents with the woodcut version of the Ugandan script Tibetan characters, and have made some research results in image pre-processing [12], layout analysis [13; 14], text line slicing [15; 16; 17; 18], character slicing [19] and data set construction [20] for the characters of Tibetan historical documents. Deep learning-based recognition of Tibetan characters has been rapidly developed in recent years and is superior to traditional recognition methods, with significant recognition results. The authors of [21] carried out a study on the recognition of Tibetan handwritten digits based on convolutional neural networks, using CNN models for training Tibetan handwritten digit samples. One study [7] carried out a study on the design and analysis of a Tibetan recognition system based on an improved algorithm of convolutional neural network and proposed a handwritten Tibetan recognition method based on an improved convolutional neural network. This paper integrated the functions of feature extraction and classifier and reduced the uncertainty factors influenced by the outside world in the feature extraction process. In [22], a deep neural network based on residual network and bi-directional long- and short-term memory recurrent neural network was constructed, and a deep neural network-based recognition method was proposed for image text recognition of Tibetan ancient woodcuts; those solved the problem of poor image quality, serious adhesion of adjacent text and large overlap of upper and lower lines in the recognition of ancient texts. In [23], a convolutional neural network CovNet was constructed for recognition, and a recognition algorithm based on the baseline features of the Ugandan script and migration learning was proposed. In order to enhance the expressiveness of the features and reduce the resolution loss during pooling, the convolutional network contained six convolutional layers, and a pooling layer was added after every two convolutional layers to improve the recognition accuracy. However, the quality and size of samples in deep learning methods have a great impact on recognition results. In Tibetan recognition, constructing a data set with complete categories and sufficient samples is a priority. There are no publicly available marked samples for images of ancient Tibetan characters, and the above methods for Tibetan character recognition all require marked samples. To address the above problems, this paper uses a component-based sample construction method to synthesize Tibetan ancient text character image samples using Tibetan character components. It also constructs a Tibetan unique character image dataset containing 519 categories of Tibetan characters, with 1000 samples for each category. It introduces character baseline information and synthesis image samples of the part of Tibetan unique characters above the baseline and image samples below the baseline. There are 910 samples above the baseline and 81,900 samples below the baseline.