## 1 Introduction  
Image classification has always been one of the prominent topics in deep learning, and Chinese character recognition is one application of it. Traditionally, optical character recognition (OCR) has been used for text recognition and it has achieved good results. Large-scale image classification is an important and challenging task in the field of computer vision, which plays an essential role in facilitating OCR methods. For example, the number of classes for a Chinese OCR system could be as high as 13,070. When performing large-scale classification, the amount of data in each category is considered the most important factor. By contrast, if a classifier is divided into an excessive number of characters, the accuracy decreases with an increase in the numbers of characters. ID card information verification is widely performed for multiple purposes on various occasions, such as for opening bank accounts or making deposits, hotel check-in, clinic registration, identity verification at facility entrances, and pick-up services for purchaseditems. The development of a system designed to automatically identify personal data on ID cards is expected to provide considerable convenience for both customers and service providers. It also saves human resources and reduces the possibility of errors. It not only saves time but also reduces physical contact, especially when infectious diseases are prevailing, making it especially important and safe. [PERSON] et al. [1] developed an OCR system to identify new ID cards issued in Indonesia. Similarly, [PERSON] et al. [2] used Maximally Stable Extremal Regions (MSER) to detect preprocessed Indonesian ID card images, and found the areas where the text was located. [PERSON] et al. [3] applied a series of image processing techniques, such as image binarization, Sobel edge detection, and morphology to mark the areas of text on citizen ID cards. Then, Google Tesseract was used as a primary framework to preform character recognition. Their approach correctly identified citizen ID cards at a rate of over 90%. [PERSON] et al. [4] designed a network model called an efficient and accurate scene text detector, which was able to accurately extract the text area. Compared with the MERS-based algorithm, it was more adaptable to natural noise and faster. On arriving at a hotel, conventionally, one must check-in via the reception staff. The traditional method involves manual data entry on a workstation computer through the manual confirmation of identity documents. Although a bar code is included in contemporary ID cards that records personal information, only government agencies or specific institutions can access and use it owing to privacy issues; ordinary hotels cannot use this feature. Human error is typical under such settings. For example, during peak tourist season, the influx of a large number of customers may easily cause the reception staff to panic or otherwise perform imperfectly, leading to data entry errors. Moreover, customers' privacy may be easily violated and their personal information may be exploited by the malicious actions of staff at the reception desk itself. In this study, we aim to establish a self-service check-in system, as shown in Figure 1, utilizing the proposed large-scale printed Chinese OCR with deep learning and transfer learning. When a user presents their ID card to the camera device, the system can automatically recognize their personal information and complete the follow-up check-in procedures, which not only solves the manual error problem but also avoids the possibility of malicious actions. Despite the fact that handwritten text recognition algorithms have been established, further research on text recognition on ID cards is necessary to account for differences in background and lighting. Background noise tends to cause issues with character segmentation and recognition. Furthermore, after a long period of use, ID cards can develop scratches and damage, and the lamination layer of their cover can yellow with age.