Nandanwar, Lokesh and Shivakumara, Palaiahnakote and Kundu, Sayani and Pal, Umapada and Lu, Tong and Lopresti, Daniel (2021) Chebyshev-Harmonic-Fourier-Moments and Deep CNNs for Detecting Forged Handwriting. In: 25th International Conference on Pattern Recognition (ICPR), 10-15 Jan 2021.
Full text not available from this repository.Abstract
Recently developed sophisticated image processing techniques and tools have made easier the creation of high-quality forgeries of handwritten documents including financial and property records. To detect such forgeries of handwritten documents, this paper presents a new method by exploring the combination of Chebyshev-Harmonic-Fourier-Moments (CHFM) and deep Convolutional Neural Networks (D-CNNs). Unlike existing methods work based on abrupt changes due to distortion created by forgery operation, the proposed method works based on inconsistencies and irregular changes created by forgery operations. Inspired by the special properties of CHFM, such as its reconstruction ability by removing redundant information, the proposed method explores CHFM to obtain reconstructed images for the color components of the Original, Forged Noisy and Blurred classes. Motivated by the strong discriminative power of deep CNNs, for the reconstructed images of respective color components, the proposed method used deep CNNs for forged handwriting detection. Experimental results on our dataset and benchmark datasets (namely, ACPR 2019, ICPR 2018 FCD and IMEI datasets) show that the proposed method outperforms existing methods in terms of classification rate.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Orthogonal rotation invariant moments; Fourier moments; Forgery detection; Fraud document identification; Forged handwriting detection |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 21 Feb 2022 05:17 |
| Last Modified: | 21 Feb 2022 05:17 |
| URI: | http://eprints.um.edu.my/id/eprint/26311 |
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