###### Abstract  
As there is a lack of public mark samples of Tibetan historical document image characters at present, this paper proposes an unsupervised Tibetan historical document character recognition method based on deep learning (UD-CNN). Firstly, using the Tibetan historical document character component, the Tibetan historical document character sample data set is constructed for model-aided training. Then, the character baseline information is introduced, and a fine-grained feature learning strategy is proposed. For the samples above and below the baseline, the Up-CNN recognition model and Down-CNN recognition model are established. The convolution neural network model is trained and optimized for the samples above and below the baseline, respectively, to improve the recognition accuracy. The experimental results show that the proposed method obviously affects the unmarked character classification and recognition of real Tibetan historical document images. The recognition rate of Top5 can reach 92.94%, and the recognition rate of Top1 can be increased from 82.25% to 87.27% using the CNN model only. character images of Tibetan historical document; character parts; character baseline; CNN; unsupervised +
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the preservation of ancient Tibetan texts, it is now the trend to analyze and recognize the images and convert them into internal codes in an automated way. Deep learning techniques have been successfully used in various text recognition [2; 3], essentially revolutionizing research in the field of Tibetan character recognition [4; 5; 6; 7]. In recent years, these methods have been initially used with some success in Tibetan online handwritten character recognition, Tibetan printed character recognition, Tibetan offline handwritten character recognition and Tibetan character recognition in natural scenes. However, existing deep learning recognition techniques rely on a large amount of annotated data, and it is very easy to overfit the deep model by using a small number of annotated samples directly. Moreover, it requires a huge annotation cost to obtain a large number of annotated images, and it is currently impossible to obtain many annotated images of the characters in Tibetan documents. Therefore, it is an important research problem to study how to use a small amount of annotated data or for zero-sample character recognition of ancient Tibetan documents. To address the above problems, this paper proposed a deep learning-based unsupervised recognition method for Tibetan ancient text character images. Firstly, a sample of Tibetan historical document character images is synthesized based on Tibetan character parts, and the synthesized sample assists in training a CNN classifier model; secondly, character baseline information is introduced to extract fine-grained features of the character images; then, the recognition process is optimized based on proportional and probability thresholds. The proposed method has significantly improved the recognition rate of unlabeled samples of Tibetan ancient Ugandan script character images. The specific work and innovation points are as follows:  
(1) At present, there is no publicly available sample set of ancient Tibetan Chinese characters in the Ugandan script. For commonly used Tibetan ancient Chinese characters, a sample set of Tibetan ancient Chinese character images is synthesized using Tibetan character components, and the sample set is divided into 519 categories, with 1000 of each category and a total of 519,000. (2) With the introduction of character baseline information, this paper synthesizes the image samples above the baseline and below the baseline of ancient Tibetan characters. There are 13 categories of synthesized samples above the baseline, 70 of each category, totaling 910; 273 categories of synthesized samples below the baseline, 300 of each category, totaling 81,900.