Name
Title | ||
|---|---|---|
Fourier Coefficients for Fraud Handwritten Document Classification through Age Analysis |
Abstract | ||
|---|---|---|
As new digital technologies emerge to improve living style, at the same time, it also lead to increase crimes. Unlike existing approaches that use content of handwriting for fraud/forged document identification, in this paper we propose a novel approach that explores the quality of handwritten documents by considering both foreground and background information to identify whether it is old or new. The proposed approach works based on the fact that if a fraud document is created with some gaps after the original one, the fraud document happened to be a new one and the original happened to be an old one in this work. To identify whether a given handwritten document is old or new with gaps, we propose to divide Fourier coefficients of the input image into positive and negative coefficient images, and then reconstruct respective images to conquer two reconstructed ones. The contrast of the reconstructed images obtained before and after divide-conquer is studied to analyze the ages of the document based on image quality. The proposed approach finds a unique relationship between reconstructed images, obtained before and after divide-conquer, to identify the input image as old or new. To evaluate the proposed approach, we conduct experiments on our own handwritten dataset and a standard database, namely, Google-LIFE magazine. Comparative studies with the existing approaches show that the proposed approach outperforms the existing approaches in terms of classification rate. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICFHR.2016.0018 | 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) |
Keywords | Field | DocType |
Fraud document,Forged document,Fourier coefficients,Divide and conquer,Document aging | Document classification,Iterative reconstruction,Handwriting,Pattern recognition,Computer science,Image quality,Feature extraction,Fourier series,Artificial intelligence,Distortion,Classification rate | Conference |
ISSN | ISBN | Citations |
2167-6445 | 978-1-5090-0982-4 | 1 |
PageRank | References | Authors |
0.37 | 10 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raghunandan K. Srinivas | 1 | 8 | 1.48 |
| Palaiahnakote Shivakumara | 2 | 774 | 64.90 |
| B. J. Navya | 3 | 1 | 1.38 |
| G. Pooja | 4 | 1 | 0.37 |
| Navya Prakash | 5 | 1 | 0.37 |
| G. Hemantha Kumar | 6 | 222 | 27.92 |
| Umapada Pal | 7 | 1477 | 139.32 |
| tong lu | 8 | 372 | 67.17 |