Finally, we perform a comparison with the best ticket recognition model from the ICDAR2019 invoice competition. Furthermore, according to the characteristics of the financial ticket text, in order to obtain higher recognition accuracy, the loss function, Region Proposal Network (RPN), and Non-Maximum Suppression (NMS) are improved to make FTFDNet focus more on text. Second, regarding the fixed format types of financial tickets (accounting for 68.27% of the total types of tickets), we propose a simple yet efficient network named the Financial Ticket Faster Detection network (FTFDNet) based on a Faster R-CNN. These recognition patterns can meet almost all types of financial ticket recognition needs. Therefore, this paper first analyzes the different features of 482 kinds of financial tickets, divides all kinds of financial tickets into three categories, and proposes different recognition patterns for each category. Moreover, none of the methods provides a detailed analysis of both the types and content of tickets. In addition, the precision and speed of their recognition models cannot meet the requirements of practical financial accounting systems.
However, first, their approaches only cover a few types of tickets. At present, a few works have applied deep learning methods to financial ticket recognition. In the financial accounting field, the rapid increase in the number of financial tickets dramatically increases labor costs hence, using a deep learning method to relieve the pressure on accounting is necessary. Currently, deep learning methods have been widely applied and thus promoted the development of different fields.
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