Chebyshev-Harmonic-Fourier-Moments and Deep CNNs for Detecting Forged Handwriting

• Main Authors: Nandanwar, Lokesh, Shivakumara, Palaiahnakote, Kundu, Sayani, Pal, Umapada, Lu, Tong, Lopresti, Daniel
• Format: Conference or Workshop Item
• Published: 2021
• Subjects: QA76 Computer software; TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.um.edu.my/26311/; https://doi.org/10.1109/ICPR48806.2021.9412179

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.