# Signature Verification using Geometrical Features and Artificial Neural Network Classifier

arXiv: https://arxiv.org/abs/2108.02029 · HTML: https://mlsanity.com/paper/2108.02029/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification](https://mlsanity.com/paper/1707.02131/) (2017)
- [Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1705.05787/) (2017)
- [Online Signature Verification using Recurrent Neural Network and Length-normalized Path Signature](https://mlsanity.com/paper/1705.06849/) (2017)
- [Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1604.00974/) (2016)
- [Fixed-sized representation learning from Offline Handwritten Signatures of different sizes](https://mlsanity.com/paper/1804.00448/) (2018)
- [Analyzing features learned for Offline Signature Verification using Deep CNNs](https://mlsanity.com/paper/1607.04573/) (2016)

## Built on by (most cited first, 1 of 1)

- [SURDS: Self-Supervised Attention-guided Reconstruction and Dual Triplet Loss for Writer Independent Offline Signature Verification](https://mlsanity.com/paper/2201.10138/) (2022)

## Abstract (arXiv)

Signature verification has been one of the major researched areas in the field of computer vision. Many financial and legal organizations use signature verification as access control and authentication. Signature images are not rich in texture; however, they have much vital geometrical information. Through this work, we have proposed a signature verification methodology that is simple yet effective. The technique presented in this paper harnesses the geometrical features of a signature image like center, isolated points, connected components, etc., and with the power of Artificial Neural Network (ANN) classifier, classifies the signature image based on their geometrical features. Publicly available dataset MCYT, BHSig260 (contains the image of two regional languages Bengali and Hindi) has been used in this paper to test the effectiveness of the proposed method. We have received a lower Equal Error Rate (EER) on MCYT 100 dataset and higher accuracy on the BHSig260 dataset.