论文标题
基于深度学习的财务机票智能认可系统
Financial ticket intelligent recognition system based on deep learning
论文作者
论文摘要
面对签发财务机票（或账单，发票等）的快速增长，传统的手动发票报销和财务会计系统正在对财务会计师施加越来越多的负担，并消耗了过多的人力。为了解决这个问题，我们提出了一个迭代性的自我学习框架智能认可系统（FFTRS），该框架可以支持算法模型的快速迭代更新和可扩展性，这是实用财务会计系统的基本要求。此外，我们设计了一个简单而高效的财务机票更快的检测网络（FTFDNET），并旨在提高其效率和绩效。目前，该系统可以识别194种财务票，并具有自动迭代优化机制，这意味着，随着申请时间的增加，系统支持的门票类型将继续增加，并且认可的准确性将继续提高。实验结果表明，该系统的平均识别精度为97.07％，一张票的平均运行时间为175.67ms。该系统的实际价值已在商业应用中进行了测试，这为财务会计工作中的深度学习技术做出了有益的尝试。
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.
