## 1 Introduction
The intertwining of these factors makes identification more challenging. The existence of numerous Chinese characters also poses an obstacle to Chinese character recognition methods. Chinese characters, in contrast to numerals or English alphabets, number in the tens of thousands, and even the most regularly used Chinese characters are divided into almost 4000 types. Further  
Figure 1: Prototype of the self-service check-in system. more, most existing studies have focused on handwritten text, with only a few works attempting to classify or identify printed text. The collection of handwritten text, which is usually generated in a laboratory or particular area, was performed intentionally in the present work. The text image was clear, and the image quality improved. Table 1 compares the characteristics of the handwritten character images and our collected ID card character images. We used dataset synthesis, background simulation, and transfer learning to increase the flexibility and performance of the proposed ID card text recognition technique. These challenges motivated us to study the problem of a large-scale classification model for ID card character recognition. In this study, we developed an automatic OCR system to identify up to 13,070 large-scale printed Chinese characters using deep learning neural networks and fine-tuning techniques. As shown in Figure 2, our framework consists of four components. (1) Training dataset synthesis is used to generate a synthesized training set of character images with different Chinese character fonts and simulated ID card backgrounds. (2) Data augmentation is performed on the synthesized training dataset by applying rotation and zooming to increase the diversity of the images contained. (3) The synthesized training dataset is input to the modified GoogLeNet Inception network (GoogLeNet-GAP) [5] for training. (4) Finally, we collected several samples of real Chinese characters on ID cards and performed data augmentation and balancing processing for further transfer learning; finally, the recognition results were obtained as the output. The main contributions of this work are summarized as follows. 1. We developed a large-scale OCR system to identify printed Chinese characters using a deep learning neural network. 2. We propose a new method to generate printed Chinese characters that simulates background noise patterns found on ID cards. To handle the range of input image changes and increase the recognition capability of a convolutional neural network (CNN) model, a massive dataset of more than 19.6 million photos was developed. 3. We improved the recognition performance of the GoogLeNet-GAP model by incorporating transfer learning. 4. The experimental results demonstrate the effectiveness of the proposed framework; the accuracy of the large-scale 13,070-character recognition system was as high as 99.39%, as evaluated on our dataset of images of real ID cards. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Handwritten Text** & **Our Input Text** \\ \hline Sample Image & & \\ “North” & & \\ \hline Complexity & High & High \\ \hline Background & Clean & **Noisy** \\ \hline Image Size & Specify & **Small** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between handwritten text and our ID card text.