Research on All-content Text Recognition Method for Financial Ticket Image

With the development of the economy, the number of financial tickets increases rapidly. The traditional manual invoice reimbursement and financial accounting system bring more and more burden to financial accountants. Therefore, based on the research and analysis of a large number of real financial ticket data, we designed an accurate and efficient all contents text detection and recognition method based on deep learning. This method has higher recognition accuracy and recall rate and can meet the actual requirements of financial accounting work. In addition, we propose a Financial Ticket Character Recognition Framework (FTCRF). According to the characteristics of Chinese character recognition, this framework contains a two-step information extraction method, which can improve the speed of Chinese character recognition. The experimental results show that the average recognition accuracy of this method is 91.75\% for character sequence and 87\% for the whole ticket. The availability and effectiveness of this method are verified by a commercial application system, which significantly improves the efficiency of the financial accounting system

Financial ticket intelligent recognition system based on deep learning

Facing the rapid growth in the issuance of financial tickets (or bills, invoices etc.), traditional manual invoice reimbursement and financial accounting system are imposing an increasing burden on financial accountants and consuming excessive manpower. To solve this problem, we proposes an iterative self-learning Framework of Financial Ticket intelligent Recognition System (FFTRS), which can support the fast iterative updating and extensibility of the algorithm model, which are the fundamental requirements for a practical financial accounting system. In addition, we designed a simple yet efficient Financial Ticket Faster Detection network (FTFDNet) and an intelligent data warehouse of financial ticket are designed to strengthen its efficiency and performance. At present, the system can recognize 194 kinds of financial tickets and has an automatic iterative optimization mechanism, which means, with the increase of application time, the types of tickets supported by the system will continue to increase, and the accuracy of recognition will continue to improve. Experimental results show that the average recognition accuracy of the system is 97.07%, and the average running time for a single ticket is 175.67ms. The practical value of the system has been tested in a commercial application, which makes a beneficial attempt for the deep learning technology in financial accounting work