## 4 Experiments
### Synthetic Sample Identification Performance Analysis  
The data set includes real samples obtained from ancient documents and synthesized samples based on components. These two types of samples have similar glyphs but exhibit differences. The real samples have highly imbalanced categories, with significant variations in sample numbers across different categories. In contrast, the synthesized samples have balanced categories with consistent sample numbers. The model is trained using synthetic samples. The trained model is then used to identify unlabeled real samples, obtaining category information for classifying labeled samples. Introducing baseline information effectively improves the accuracy of real sample identification. In order to train the optimized model as well as analyze the sample data for performance, training, experiments and analysis are performed for the synthetic sample set, the above-baseline sample set and the below-baseline sample set. The parameter setting in the experiment is to select an initial value based on the team's previous research and get it after several parameter adjustment experiments. Under the final parameters, the network convergence is faster, and the recognition rate of Tibetan characters is higher. (1) A total of 519 classes of labeled synthetic samples with a high frequency of occurrence are selected. There are 1000 samples in each class. These labeled synthetic samples are used to train the CNN model. The training samples are randomly divided into training, validation, and test sets. The proportions for the division are 70%, 10% and 20% respectively. The following parameter settings were experimentally obtained for better model performance. The optimizer adopts the stochastic gradient descent method. The number of training samples per batch is 800. The initial learning rate is 0.1. The maximum number of training rounds is 10 rounds. In order for the model to converge better to the minimal value point, the dynamic learning rate change method is used. After every two training rounds, the learning rate is adjusted as follows: the learning rate for the current round equals the learning rate from the previous round multiplied by 0.1. The training took 22 min and 22 s; the experimental results are shown in Figure 9.  
It can be seen from Figure 9 that the proposed model performs well on the synthetic sample set. It reaches stability at about 500 iterations, and the accuracy of the training set is 99.375%. The accuracy of the validation set is 99.458% at the 500 th iteration, with a loss of 0.0210 for the training set and 0.0207 for the validation set. The accuracy of the validation  
Figure 9: CNN model training process. set is 99.84%, the accuracy of the test set is 99.80%, and the overall recognition rate of the synthetic data set is 99.93%. (2) There are 13 classes of above-baseline synthetic samples, 70 of each class, totaling 910, and the recognition model of above-baseline characters (Up-CNN) is trained by the synthetic above-baseline samples. The samples were randomly divided into training, validation and test sets with the proportions of 70%, 10% and 20%, respectively. The experimental parameters are set as follows: random gradient descent, the number of samples per batch is 1000, the initial learning rate is 0.1, the maximum number of rounds is 1000, and the learning rate changes with the number of iterations in segments. The specific variation rule is used for every 150 rounds of training, and the learning rate = learning rate of the previous round * 0.1. The training took 3 min and 15 s, and the experimental results are shown in Figure 10.  
It can be seen from Figure 10 that it stabilizes at about 24 iterations, the recognition accuracy of the training set of the 24 th iteration is 100%, the recognition rate of the validation set is 100%, the loss of the training set is 0.0021, and the loss of the validation set is 0.0032.