Financial ticket intelligent recognition system based on deep learning

TL;DR

This paper presents an intelligent recognition system for financial tickets using deep learning, featuring iterative self-learning, high accuracy, and efficiency, which enhances financial accounting processes and supports continuous system improvement.

Contribution

It introduces FFTRS, an iterative self-learning framework with a novel detection network and data warehouse, enabling high accuracy and extensibility in financial ticket recognition.

Findings

Recognition accuracy of 97.07%

Supports 194 types of financial tickets

Average recognition time is 175.67ms

Abstract

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.