Signature recognition

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

Performance evaluation of handwritten signature recognition in mobile environments

Abstract: The utilisation of biometrics in mobile scenarios is increasing remarkably. At the same time, handwritten signature recognition is one of the modalities with highest potential of use for those applications where customers are used to sign in those traditional processes. However, several improvements have to be made in order to reach acceptable levels of performance, reliability and interoperability. The evaluation carried out in this study contributes with multiple results obtained from 43 users signing 60 times, divided in three sessions, in eight different capture devices, being six of them mobile devices and the other two digitisers specially made for signing and used as a baseline. At each session, a total of 20 signatures per user are captured by each device, so that the evaluation here reported a total of 20 640 signatures, stored in ISO/IEC 19794–7 format. The algorithm applied is a DTW-based one, particularly modified for mobile environments. The results analysed include inter-operability, visual feedback and modality tests. One of the big challenges of this research was to discover if the handwritten signature modality in mobile devices should be split into two different modalities, one for those cases when the signature is performed with a stylus, and another when the fingertip is used for signing. Many relevant conclusions have been collected and, over all, multiple improvements have been reached contributing to future deployments of biometrics in mobile environments.

Dynamic programming optimisation for on-line signature verification

Abstract: We focus on the use of the dynamic time warping (DTW) technique in the signature verification area. The DTW algorithm originates from the field of speech recognition, where it is a highly appreciated component of speaker specific isolated word recognisers. A few years ago the DTW algorithm was successfully introduced in the area of online signature verification. The characteristics of speech recognition and signature verification are however rather different. Starting from these dissimilarities, our objective is to extract an alternative DTW approach that is better suited to the signature verification problem.

On-line signature verification based on altitude and direction of pen movement

Abstract: A signature is widely used to authorize who issued the document. However, a signature has ambiguity, and it is difficult to distinguish the authentic signature from the mimicked signature only by the bit-mapped pattern. It is expected that the altitude and the azimuth of the gripped pen under signing depends on the shape of the writer's hand and the habit of writing. We propose a new on-line writer authentication system using the pen altitude, pen azimuth, shape of signature, and writing pressure in real time. From experimental results with writing information by 24 writers, it is found that the authentication rate of 98% is obtained.

A log-linear discriminative modeling framework for speech recognition.

Abstract: Conventional speech recognition systems are based on Gaussian hidden Markov models (HMMs). Discriminative techniques such as log-linear modeling have been investigated in speech recognition only recently. This thesis establishes a log-linear modeling framework in the context of discriminative training criteria, with examples from continuous speech recognition, part-of-speech tagging, and handwriting recognition. The focus will be on the theoretical and experimental comparison of different training algorithms. Equivalence relations for Gaussian and log-linear models in speech recognition are derived. It is shown how to incorporate a margin term into conventional discriminative training criteria like for example minimum phone error (MPE). This permits to evaluate directly the utility of the margin concept for string recognition. The equivalence relations and the margin-based training criteria lead to a unified view of three major training paradigms, namely Gaussian HMMs, log-linear models, and support vector machines (SVMs). Generalized iterative scaling (GIS) is traditionally used for the optimization of log-linear models with the maximum mutual information (MMI) criterion. This thesis suggests an extension of GIS to log-linear models including hidden variables, and to other training criteria (e.g. MPE). Finally, investigations on convex optimization in speech recognition are presented. Experimental results are provided for a variety of tasks, including the European Parliament plenary sessions task and Mandarin broadcasts.

Methods and systems for slant compensation in handwriting and signature recognition

Abstract: Various computer-implemented methods are provided. One method for signature recognition includes identifying a vertical element of a signature and determining a tilt angle of the vertical element. Tilt angle is defined by a line that is approximately parallel to the vertical element. In addition, the method includes determining a tilt factor. Tilt factor is defined as a horizontal offset between the tilt angle and a reference tilt as a function of distance from a signature baseline. The method further includes altering the signature using the tilt factor and comparing the altered signature to known signature(s) to determine if the altered signature matches one of the known signature(s). Another method includes altering the signature using a predetermined tilt factor and comparing the altered signature to known signature(s). If the altered signature does not match one of the known signature(s), these steps may be repeated with different predetermined tilt factors.