## 4 Experimental Results
### Mixed Data Model and Transfer Learning Model Results
#### 4.3.2 Transfer Learning Model  
One of the main advantages of transfer learning is that a pre-trained model can quickly adapt to new data through fine-tuning training using small amounts of real data. To evaluate the performance of transfer learning, we fine-tuned various classification models with different amounts of real data by providing 10 and 20 augmentation samples per character. However, if only a small number of characters are used to fine-tune large-scale character classification, the model becomes biased towards a small number of characters and thus loses the recognition rate for other characters. To avoid deviations, data balancing is applied by recruiting the synthetic data generated in Section 3.1 for the remaining characters and then performing training. Table 8 presents the results of fine-tuning using three different amounts of real character data (100, 200, and 294 characters) for five training scenarios. We supplied varying numbers of augmented samples per character to perform transfer learning (namely 1, 5, 10, 15, and 20), and subsequently, assessed the recognition performance. After the data were generated and balanced, the problems of insufficient data and learning deviation were solved simultaneously. Table 8 illustrates that the data augmentation improved the recognition outcomes, with boldface indicating the best performance. As may  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\# of Training Samples** & **294 Classes** & **1812 Classes** & **4945 Classes** & **13,070 Classes** \\
**per Character** & **(Characters)** & **(Characters)** & **(Characters)** & **(Characters)** \\ \hline
300 & 51.40\% & 77.91\% & 76.52\% & 66.51\% \\
600 & 90.62\% & 88.21\% & 89.99\% & 83.72\% \\
900 & 95.71\% & 91.91\% & 89.42\% & 88.51\% \\
1500 & 95.35\% & 95.28\% & 90.23\% & 92.17\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of various large-scale GoogLeNet-GAP models with different numbers of training samples per character.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\# of Classes** & **600 Training Samples per Character** & **900 Training Samples per Character** \\ \cline{2-5}
**(Characters)** & **Original Model** & **Mixed Data Model** & **Original Model** & **Mixed Data Model** \\ \hline
294 & 90.62\% & **96.84\%** & 95.71\% & **98.51\%** \\
1812 & 88.21\% & **96.22\%** & 91.91\% & **95.03\%** \\
4945 & 89.99\% & **93.54\%** & 89.42\% & **96.16\%** \\
13,070 & 83.72\% & **91.95\%** & 88.51\% & **93.93\%** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of the original model and the mixed data model with 600 and 900 training samples per character.  
be observed from Table 8, our transfer model was able to improve the recognition accuracy of characters on printed ID cards with fine-tuning. Figure 11 shows that the character recognition performance was dramatically improved when the models were fine-tuned using more real data. Figure 12 shows that the character recognition performance was improved when the models were fine-tuned using more augmentation data per character.  
Figure 11: Character recognition performance was improved dramatically when the models were fine-tuned using more numbers of real character data (100, 200, and 294) for two training scenarios, namely, (**a**) 5 augmentation samples per character, and (**b**) 20 augmentation samples per character.  
Figure 12: Character recognition performance was improved when the models were fine-tuned using more numbers of augmentation data (5, 10, 15, and 20) for two training scenarios, namely, (**a**) 200 real characters, and (**b**) 294 real characters.