Few-Sample Generation of Amount in Figures for Financial Multi-Bill Scene Based on GAN
Bibliographic Data
| ID | 22107779 |
|---|---|
| Authors | Zhi-Ri Tang (0000-0002-0900-6500, Wuhan University), Qiqi Chen (0000-0001-6062-3216, Shanghai Jiao Tong University), Qi-Qi Chen (Shanghai Jiao Tong University), Poly Z H Sun (0000-0001-6156-1766, Shanghai Jiao Tong University), Pengwen Xiong (0000-0002-0623-8592, Nanchang University), Bao-Hua Zhang (Industrial and Commercial Bank of China, Shanghai, China), Baohua Zhang (0000-0003-3231-0233, Industrial and Commercial Bank of China), Lu Jiang (0000-0003-4522-3614, Industrial and Commercial Bank of China), Edmond Q Wu (0000-0003-1301-9870, Shanghai Jiao Tong University) |
| Year | 2023 |
| Volume | 10 |
| Issue | 3 |
| Pages | 1326-1334 |
| Publication date | 2023-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2021.3136602 |
| OpenAlex | W4205220354 |
| Language | EN |
| Citations received | 3 |
| References cited | 24 |
Recognition of amount in figures in the financial multi-bill scenes is crucial for the automatic banking business. However, the diversity of banking business and the limitation of customer data privacy determine that it is difficult to collect a large number of sample datasets. Aiming at the problem of insufficient training data in multi-bill scenes and the low accuracy of the detection model, this article proposes a new generative adversarial network (GAN) to generate new samples and to expand the bill dataset, which is then adopted to train a framework for recognition of the bill amount. In the proposed WGAN-SA, a residual block is adopted as the basic structure of the generator and the discriminator, and the self-attention mechanism is also utilized to improve the generation performance. In addition, Wasserstein distance is utilized to measure the distance between real and synthetic samples. Experimental results on the benchmark dataset and comparisons with state-of-the-art works show that our proposed WGAN-SA can effectively improve the few-sample learning performance. Besides, experiments on the bill dataset verify that our method can solve the problem of model collapse and has the ability to generate images of the amount in figures with better fidelity and variety, which is also helpful to achieve better bill amount recognition performance compared with other latest works
Algorithm · Data mining · Discriminator · Fidelity · Machine learning · Residual · Telecommunications · Computer Science · Currency Recognition and Detection · Generative Adversarial Networks and Image Synthesis · Handwritten Text Recognition Techniques · Artificial Intelligence
| Citations per year | 1.5 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |