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1 sty 2018 · In this paper, a solution based on Convolutional Neural Network (CNN) is presented where the model is trained with a dataset of signatures, and predictions are made as to whether a provided signature is genuine or forged.
- Offline Signature Recognition and Forgery Detection using Deep Learning
It also proposed a novel method for signature recognition...
- Offline Signature Recognition and Forgery Detection using Deep Learning
Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. It is essential in preventing falsification of documents in numerous financial, legal, and other commercial settings.
In order to effectively combat handwritten signature forgery, this study intends to create an automated system utilizing a deep neural network (DNN) especially a simple and efficient Convolutional Neural Network (CNN) that can determine if a given signature is real or faked.
Handwritten-Signature-Forgery-Detection. Convolutional Neural Network (CNN) model is trained with a dataset of signatures, and predictions are made as to whether a provided signature is genuine or forged. The CNN architecture is implemented using various python libraries such as opencv, sklearn, scikit images, numpy, matplotlib, scipy, pillow etc.
1 sty 2020 · It also proposed a novel method for signature recognition and signature forgery detection with verification using Convolution Neural Network (CNN), Crest-Trough method and SURF algorithm & Harris corner detection algorithm.
This study has proposed an algorithm known as Learning based Signature Forgery Detection (LbSFD), which exploits pipeline of multiple DL models such as CNN, VGG16 and Siamese. All the models are CNN variants used to improve efficiency in signature forgery detection.
This groundbreaking research paper introduces SigScatNet, an innovative solution to combat this issue by harnessing the potential of a Siamese deep learning network, bolstered by Scattering wavelets, to detect signature forgery and assess signature similarity.
