| Journal: | Computación y sistemas |
| Database: | |
| System number: | 000560391 |
| ISSN: | 1405-5546 |
| Authors: | Galeana Pérez, Deysy Bayro Corrochano, Eduardo1 |
| Institutions: | 1CINVESTAV, Deparment Electrical Engineering and Computer Science, Guadalajara. México |
| Year: | 2018 |
| Season: | Oct-Dic |
| Volumen: | 22 |
| Number: | 4 |
| Pages: | 1065-1076 |
| Country: | México |
| Language: | Inglés |
| English abstract | This article presents a robust and efficient system for euro and Mexican banknote recognition. A high banknote recognition and classification rate was achieved using neural networks and deep learning with real scene images taken with both sunlight and artificial light. Without extracting characteristics by hand, the convolutional neural networks was fed with raw images. Analysis and experiments were carried out on banknotes based on key features, such as; watermarks, portraits on the bills, bill value written in words and numbers, and the complete banknotes. It was concluded that both the color information and some regions of the banknotes, as well as the banknote denomination written in words and numbers and the complete banknote, is the appropriate information to achieve a high rate of banknote classification and recognition. The experimental results show that the proposed approach is promising with quite remarkable results; it performs an efficient and robust classification using real scene images taken with both sunlight and artificial light and is invariant to banknote rotation and translation. A high recognition rate was achieved for Mexican banknotes and for euros. At present, the results contained herein are an improvement over those reported in the state of the art. |
| Keyword: | Banknote recognition, Convolutional neural networks, Deep learning |
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