Classification of Financial Tickets Using Weakly Supervised Fine-Grained Networks

Abstract

Facing the rapid growth in the issuance of financial tickets, traditional manual invoice reimbursement methods are imposing an increasing burden on financial accountants and consuming excessive manpower. There are too many categories of financial ticket that need to be classified with high accuracy. Therefore, we propose a Financial Ticket Classification (FTC) network based on weakly supervised fine-grained classification discriminative filter learning networks, which greatly improves the work efficiency of financial accountants. The FTC network adopts an end-to-end network structure and uses a deep convolution network to extract highly descriptive features. By using a fully convolutional network (FCN), this method reduces the depth and width of the whole network and avoids the over duplication of features and the overconsumption of system memory. To obtain more accurate classification results, we use the large-margin softmax (L-softmax) loss function, which can make the features learned in the class more compact, make it easier to separate subclasses, and effectively prevent overfitting. Experimental results show that the proposed FTC network achieves both high accuracy (up to 99.36%) and high processing speed, which perfectly meets the requirements of accurate and real-time classification for financial accounting applications.