Handwritten Signature Verification using Deep Learning

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%

Feature Representation for Online Signature Verification

Biometrics systems have been used in a wide range of applications and have improved people authentication. Signature verification is one of the most common biometric methods with techniques that employ various specifications of a signature. Recently, deep learning has achieved great success in many fields, such as image, sounds and text processing. In this paper, deep learning method has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information Forensics and Securit

Offline Handwritten Signature Verification - Literature Review

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory, Tools and Applications (IPTA 2017

Signature Verification

Handwriting recognition is a process in recognizing handwritten letter images. For this project, the main purpose is to identify signature's owners to prevent from skilled forger. Therefore, the project is more focused on the signature verification rather than the character recognition. Signature verification can prevent falsification by detecting the flow ofthe curve ofthe signature and using the distance similarity. The main objective of this project is to verify the signature. The secondary objectives are to make life easier by having the signature verification system to identify the skilled forger from using someone else's credit card and to increase the security measure on credit card. The method that will be used in this project is using the neural network classification in the backpropagation network. This is because backpropagation can get the input to give the correct output, which has been used by many researchers. In implementing the prototype, a distance measure is used as the verification method but backpropagation is one of the suitable methods in designing it for future expansion. Matlab software is used in developing the system. Basically, this system will be using the Matlab software and for the hardware part by using the digitized tabiet with the pen tip in order to capture the user signature image. Beside that, some calculations will be used in measuring the signature attributes andas for the error part; there will bethepercentage ofthe error occurs

Offline Signature Verification based on Euclidean distance using Support Vector Machine

In this project, a support vector machine is developed for identity verification of offline signature based on the matrices derived through Euclidean distance. A set of signature samples are collected from 35 different people. Each person gives his 15 different copies of signature and then these signature samples are scanned to have softcopy of them to train SVM. These scanned signature images are then subjected to a number of image enhancement operations like binarization, complementation, filtering, thinning, edge detection and rotation. On the basis of 15 original signature copies from each individual, Euclidean distance is calculated. And every tested image is compared with the range of Euclidean distance. The values from the ED are fed to the support vector machine which draws a hyper plane and classifies the signature into original or forged based on a particular feature value