Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

Handwritten Courtesy Amount and Signature Recognition on Bank Cheque using Neural Network

Abstract: Bank cheques are used not only in our country but also all over the world for financial transactions. Still now Bank cheques are processed manually everyday in both developed and developing countries. The proposed system presents a recognition system of both handwritten courtesy amount and signature .To read the courtesy amount and signature, the system uses the scanned image of Bank cheque. The proposed system divided into several stages that focus on: Image preprocessing; the detection of block of courtesy amount and signature; the post processing; the segmentation of string into characters; Feature extraction of courtesy amount and signature; Neural Network recognition. At first, scanned image is converted into gray image and then it is filtered. Then detection of the courtesy amount and signature is performed using image cropping method. The cropped images are post-processed to ensure correct recognition. Then courtesy amount is segmented using the segmentation method. The segmentation module has been implemented as a recursive process. Then segmented digit and binary image of signature are passed through the feature extraction process. A rotation, translation, scaling and orientation invariant feature extraction method has been used to extract the features of the input images based on moment feature extraction method. And finally, a back propagation learning algorithm is used to train up the network and tested the performance. Overall success rate of the system is tested in different sorts of numeral and the experimental result shows satisfactory performance.

A Robust Online Signature Based Cryptosystem

Abstract: Cryptography is the backbone for the security systems. The main challenge in use of the Cryptosystems is maintaining the confidentiality of the cryptographic key. A Cryptosystem which encrypts the data using biometric features improves the security of the data and overcomes the problems of key management and key confidentiality. Fuzzy Vault Scheme proposed by Juels and Sudan [1] binds the secret key and the biometric template, so that extraction of the secret without the biometric data is infeasible. Physical signature is a biometric that is widely accepted and is used for proving the authenticity of a person in legal documents, bank transactions etc. Electronic devices such as digital tablets capture azimuth, altitude and pressure along with x any y coordinates at fixed time interval. This paper describes a robust online signature based cryptosystem to hide the secret by binding it with invariant online signature templates. The invariant templates of the signature are derived from artificial neural network based classifier. The entire signature is divided into fixed number of time slices. Important features are extracted based on the consistency of the feature in the slices of the genuine signature. Binary back propagation based neural network for each feature, each subset of slices for a user is trained by a weighted back propagation algorithm. The decisions of these networks are combined using AdaBoost algorithm. The proposed scheme is highly robust as it works well for all kinds of signatures and is independent of the number of zero crossing and high curvature points in the signature trajectory.

Developing pattern recognition systems based on Markov models: The ESMERALDA framework

Abstract: In this paper we describe ESMERALDA—an integrated Environment for Statistical Model Estimation and Recognition on Arbitrary Linear Data Arrays—which is a framework for building statistical recognizers operating on sequential data as, e.g., speech, handwriting, or biological sequences. ESMERALDA primarily supports continuous density Hidden Markov Models (HMMs) of different topologies and with user-definable internal structure. Furthermore, the framework supports the incorporation of Markov chain models (realized as statistical n-gram models) for long-term sequential restrictions and Gaussian mixture models (GMMs) for general classification tasks. ESMERALDA is used by several academic and industrial institutions. It was successfully applied to a number of challenging recognition problems in the fields of automatic speech recognition, offline handwriting recognition, and protein sequence analysis. The software is open source and can be retrieved under the terms of the LGPL.

On-line signature verification using point pattern matching algorithm

Abstract: Authentication of persons has been known as a paramount part in society. Security requirements have given biometrics much attention. Verification of the signature is one of the biometric methods used in recognition systems. This paper presents a new method to verify an online signature of a person. In features extraction step, local parameters are extracted as time functions of different dynamic properties. In recognition phase the Discrete to Continuous Algorithm is applied on features to verify the signature claimed by a person. The results obtained by the proposed algorithm show a good accuracy rate.

Local-to-Global Signature Descriptor for 3D Object Recognition

Abstract: In this paper, we present a novel 3D descriptor that bridges the gap between global and local approaches. While local descriptors proved to be a more attractive choice for object recognition within cluttered scenes, they remain less discriminating exactly due to the limited scope of the local neighborhood. On the other hand, global descriptors can better capture relationships between distant points, but are generally affected by occlusions and clutter. So, we propose the Local-to-Global Signature (LGS) descriptor, which relies on surface point classification together with signature-based features to overcome the drawbacks of both local and global approaches. As our tests demonstrate, the proposed LGS can capture more robustly the exact structure of the objects while remaining robust to clutter and occlusion and avoiding sensitive, low-level features, such as point normals. The tests performed on four different datasets demonstrate the robustness of the proposed LGS descriptor when compared to three of the SOTA descriptors today: SHOT, Spin Images and FPFH. In general, LGS outperformed all three descriptors and for some datasets with a 50–70% increase in Recall.