  Currently, deep learning methods have been widely applied in and thus
promoted the development of different fields. 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. At present, a few works have applied deep
learning methods to financial ticket recognition. However, first, their
approaches only cover a few types of tickets. In addition, the precision and
speed of their recognition models cannot meet the requirements of practical
financial accounting systems. Moreover, none of the methods provides a detailed
analysis of both the types and content of tickets. 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. These recognition patterns can meet
almost all types of financial ticket recognition needs. 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 RCNN.
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. Finally, we perform a comparison with the best ticket
recognition model from the ICDAR2019 invoice competition. The experimental
results illustrate that FTFDNet increases the processing speed by 50\% while
maintaining similar precision.
