Signature recognition

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

Offline signature verification and identification by hybrid features and Support Vector Machine

Abstract: This paper emphasised an approach for offline signature verification and identification. Two image descriptors are studied, including Pyramid Histogram of Oriented Gradients (PHOG), and a direction feature proposed in the literature. Compared with many previously proposed signature feature extraction approaches, PHOG has advantages in the extraction of discriminative information from handwriting signature images. The significance of classification framework is stressed. With the benchmarking database ||Grupo de Procesado Digital de Senales|| (GPDS), satisfactory performances were obtained from several classifiers. Among the classifiers compared, SVM is clearly superior, giving a False Rejection Rate (FRR) of 2.5% and a False Acceptance Rate (FAR) 2% for skillful forgery, which compares sharply with the latest published results on the same dataset. This substantiates the superiority of the proposed method. The related issue offline signature recognition is also investigated based on the same approach, with an accuracy of 99% on the GPDS data from SVM classification.

Character Recognition Digit Recognition Pattern Recognition Spatiotemporal Neural Networks Modular Networks Segmentation Problem

Abstract: We describe an alternate approach to visual recognition of handwritten words, wherein an image is converted into a spatio-temporal signal by scanning it in one or more directions, and processed by a suitable connectionist network. The scheme offers several attractive features including shift-invariance, explication of local spatial geometry along the scan direction, a significant reduction in the number of free parameters, the ability to process arbitrarily long images along the scan direction, and a natural framework for dealing with the segmentation/recognition dilemma. Other salient features of the work include the use of a modular and structured approach for network construction and the integration of connectionist components with a procedural component to exploit the complementary strengths of both techniques. The system consists of two connectionist components and a procedural controller. One network concurrently makes recognition and segmentation hypotheses, and another performs refined recognition...

Recognition algorithm of online handwritten signature

Abstract: This paper presents a mathematics model of handwritten signature recognition and matches time sequences from the thought of dynamic planning. The recognition algorithm was effectually examined by practice. The paper also introduces distance calculation formula based on weighting H~1 module to calculate the difference between input signatures and true signatures.

The Algorithm of On-Line Handwritten Signature Verification Based on Hide Markov Model

Abstract: An extracting method of special points in signature is presented. The special points are used as signature segmented points, and the extraction and selection of each segment features are analyzed. The algorithm of on-line handwritten signature verification based on Hidden Markov Model (HMM) is presented and examined availably by the signature database from SVC 2004 (First International Signature Verification Competition).

Applying dynamic methods in off-line signature recognition

Abstract: In this paper we present the work developed on off-line signature verification using Hidden Markov Models (HMM). HMM is a well-known technique used by other biometric features, for instance, in speaker recognition and dynamic or on-line signature verification. Our goal here is to extend Left-to-Right (LR)-HMM to the field of static or off-line signature processing using results provided by image connectivity analysis. The chain encoding of perimeter points for each blob obtained by this analysis is an ordered set of points in the space, clockwise around the perimeter of the blob. We discuss two different ways of generating the models depending on the way the blobs obtained from the connectivity analysis are ordered. In the first proposed method, blobs are ordered according to their perimeter length. In the second proposal, blobs are ordered in their natural reading order, i.e. from the top to the bottom and left to right. Finally, two LR-HMM models are trained using the parameters obtained by the mentioned techniques. Verification results of the two techniques are compared and some improvements are proposed.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.