## 4 Experimental Results
### Error Analysis and Performance Enhancement  
In Section 4.3, we demonstrate that the two proposed transfer learning methods were able to strengthen the model trained using synthesized data and real data. The experimental results show that the recognition rate was improved, and it reached more than 95% after adding a few real samples for transfer learning. Although our model achieved good recognition results under large-scale classifications, some issues remain nonetheless. Figure 13 shows some samples with incorrect recognition in the large-scale classification of 13,070 characters. There are some Chinese characters that seem to be similar however, in reality, they have completely different meanings. On ID cards with a cluttered background, the text is more likely to be disturbed by background noise. In addition, an ID card may be stained or damaged due to wear, which may result in misclassifications by the CNN model. Figure 13a displays two examples of misclassification, as mentioned above. In addition, if the text segmentation method is not sufficiently robust, it may easily cause the cropped text image to contain more than one character, or to retain only parts of the character. Figure 13b presents two examples of misclassification due to inappropriate cropping. To solve the recognition problem caused by character segmentation shifting, we utilized the projection method. First, the grayscale character image was converted to a binary image with a specific threshold to separate the character from the background. Then, binary pixels were projected in the horizontal and vertical directions separately to locate the area of the character in the image. However, the ID card images may include a noisy background, and the projection may be affected by noise disturbances. Therefore, we performed morphology processing using a 1 \(\times\) 7 vertical bar-type erosion operator on the horizontal projection image and disconnected the small adjacent blocks. Next, we search to check if there was a continuous black area from the boundary toward the center, as indicated by the red lines in Figure 14. Meanwhile, the distance between the two red lines was required to be greater than 60% of the original width or height to enclose a complete \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \# of Augmentation & \multirow{2}{*}{\# of Output Classes} & \multicolumn{4}{c}{**\# of Characters Fine-tuned in Transfer Learning**} \\ \cline{3-6}
**Samples per Character** & & **0** & **100** & **200** & **294** \\ \hline \multirow{4}{*}{1} & 294 & 90.62\% & 96.91\% & 97.25\% & 97.90\% \\  & 1812 & 88.21\% & 97.31\% & 97.25\% & 97.27\% \\  & 4945 & 89.99\% & 95.79\% & 96.97\% & 98.06\% \\  & 13,070 & 83.72\% & 95.00\% & 96.50\% & 96.06\% \\ \hline \multirow{4}{*}{5} & 294 & 90.62\% & **99.25\%** & 99.27\% & **99.41\%** \\  & 1812 & 88.21\% & **98.89\%** & 98.75\% & **99.81\%** \\  & 4945 & 89.99\% & 98.40\% & 98.26\% & 99.31\% \\  & 13,070 & 83.72\% & 97.61\% & 98.18\% & 99.19\% \\ \hline \multirow{4}{*}{10} & 294 & 90.62\% & 99.13\% & 99.47\% & 99.13\% \\  & 1812 & 88.21\% & 98.81\% & 98.56\% & 99.09\% \\  & 4945 & 89.99\% & 98.00\% & 98.56\% & 98.50\% \\  & 13,070 & 83.72\% & 98.16\% & 98.50\% & 98.77\% \\ \hline \multirow{4}{*}{15} & 294 & 90.62\% & 98.85\% & **99.56\%** & 99.33\% \\  & 1812 & 88.21\% & 98.85\% & **99.03\%** & 99.39\% \\  & 4945 & 89.99\% & 98.26\% & 98.75\% & 99.37\% \\  & 13,070 & 83.72\% & **98.34\%** & 98.56\% & 99.11\% \\ \hline \multirow{4}{*}{20} & 294 & 90.62\% & 98.75\% & 99.45\% & 99.33\% \\  & 1812 & 88.21\% & 98.58\% & 98.83\% & 99.41\% \\ \cline{1-1}  & 4945 & 89.99\% & **98.87\%** & **98.93\%** & **99.47\%** \\ \cline{1-1}  & 13,070 & 83.72\% & 97.34\% & **98.85\%** & **99.39\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Testing results on transfer model fine-tuned using three different amounts of real character data (100, 200, and 294 characters) for five training scenarios, namely, 1, 5, 10, 15, and 20 augmentation samples per character. character.