## 4 Experimental Results
### Ablation Study  
To verify that our GoogLeNet-GAP network can overcome the overfitting problem and obtain higher recognition accuracy, we compared the performances of the models with the original FC layer and the new GAP layer by providing a different number of training samples per character for the 294 output classes in the GoogLeNet model. Table 4 shows the effect of using the GAP layer as the classification output layer. Our GoogLeNet-GAP network achieved higher accuracy. Moreover, it may be observed that when additional training samples was provided, the network model performed better. Therefore, subsequent experiments will be based on the GoogLeNet-GAP model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **\# of Characters** & **\# of Images** & **Type** \\ \hline CASIA-HWDB1.0 & 3866 & 1,609,136 & Simplified Chinese \\ CASIA-HWDB1.1 & 3755 & 1,121,749 & Simplified Chinese \\ CASIA-HWDB1.2 & 3319 & 990,989 & Simplified Chinese \\ Chinese MNIST & 13,065 & 587,925 & Traditional Chinese \\ CASIA-AHCDB & 10,350 & more than 2.2 million & Traditional Chinese \\
**Ours** & **13,070** & **19.6 million** & Traditional Chinese \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the common Chinese character datasets and the proposed dataset. In addition, for the fully connected nature of FC, when the number of character categories increased, the number of FC layer parameters also increased dramatically. Thus, the chances of overfitting increased sharply. When using GAP, because its hidden layer does not have trainable parameters, the parameters of the output layer are only increased on increasing the number of classifications. Therefore, compared with FC, GAP has fewer parameters and is less likely to cause overfitting. Furthermore, the use of fewer parameters can increase the computational speed of the CNN. Table 5 shows the number of parameters used in the FC and GAP for various classification tasks. The computational cost of the network training phase is determined by the amount of classifications and training samples per character required. It may also be identified in terms of the computing resources and software environment used. Additional experiments on the quantitative assessment of network computational time have been performed. Regarding the GoogLeNet model, Table 5 compares the computational complexity of FC and GAP. As shown, using GAP minimized the time spent on training and recognition. This research is focused on a real-time application with the goal of deploying the proposed OCR system on a remote server, allowing smartphones to send images to the backend for text recognition. Thus, the character recognition process requires immediate execution. The recognition time for each character was approximately 0.0026 s, as shown in Table 5. Hence, the proposed approach can be used in real-time scenarios. According to the Ministry of Education of Taiwan, 4803 standard Chinese characters are frequently used in daily life. There are 1802 characters commonly used in addresses according to the postal system. In addition to our target of the large-scale classification of 13,070 characters, we also conducted experiments on the models of 1812 outputs (1802 characters plus 10 digits) and 4945 outputs (the union of 4803 standard Chinese characters, 1802 characters in postal system, and 10 digits) for the postal system and education system, respectively. Table 6 indicates that the GoogLeNet-GAP maintained classification performance, even when the number of model outputs was expanded from 1812 to 13,070. Furthermore, all the character recognition tasks were performed with an accuracy exceeding 90% when the number of training samples per character increased to 900. The system achieved the best performance on average, given 1500 training samples per character. Nevertheless, the more training samples provided, the more time required to train the model.