| Full text | |
| Author(s): |
Saire, Darwin
;
Tabbone, Salvatore
;
IEEE COMP SOC
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR); v. N/A, p. 8-pg., 2021-01-01. |
| Abstract | |
The main topic of this work is on the detection of counterfeit documents and especially banknotes. We propose an end-to-end learning model using a deep learning approach based on Adapnet++ which manages feature extraction at multiple scale levels using several residual units. Unlike previous models based on regions of interest (ROI) and high-resolution documents, our network is feed with simple input images (i.e., a single patch) and we do not need high resolution images. Besides, discriminative regions can be visualized at different scales. Our network learns by itself which regions of interest predict the better results. Experimental results show that we are competitive compared with the state-of-the-art and our deep neural network has good ability to generalize and can be applied to other kind of documents like identity or administrative one. (AU) | |
| FAPESP's process: | 19/18678-0 - Semantic Segmentation Using Hourglass Learning Model |
| Grantee: | Darwin Danilo Saire Pilco |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |