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Signature recognition

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


Papers
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01 Jan 2013
TL;DR: A fundamentally new approach to HR based on 2-D geometric warping of word images that is significantly more accurate than an existing whole-word approach used for word-spotting, and may also be better than HMM-based HR approaches.
Abstract: Warping-Based Approach to Offline Handwriting Recognition Douglas J. Kennard Department of Computer Science, BYU Doctor of Philosophy An enormous amount of the historical record is currently trapped in non-indexed handwritten format. Even after being scanned into images, only a minute fraction of the existing records can be manually transcribed / indexed with reasonable amounts of time and cost. Although progress continues to be made with automatic handwriting recognition (HR), it is not yet good enough to replace manual transcription or indexing. Much of the recent HR work has focused on incremental improvements to methods based on Hidden Markov Models (HMMs) and other similar probabilistic approaches. In this dissertation we present a fundamentally new approach to HR based on 2-D geometric warping of word images. The results of our experimentation indicate that our approach is significantly more accurate than an existing whole-word approach used for word-spotting, and may also be better than HMM-based HR approaches. Since it is a completely new method, we also believe there is potential for improvement and future work that builds on this approach. In addition, we demonstrate that the approach can be used effectively in the related application domain of signature verification and forgery detection.

1 citations

01 Jan 1989
TL;DR: A simulated shape recognition system using feature extraction was built as an aid for designing robot vision systems to study the effects of image resolution and feature selection on the performance of a vision system that tries to identify unknown 2-D objects.
Abstract: A simulated shape recognition system using feature extraction was built as an aid for designing robot vision systems. The simulation allows the user to study the effects of image resolution and feature selection on the performance of a vision system that tries to identify unknown 2-D objects. Performance issues that can be studied include identification accuracy and recognition speed as functions of resolution and the size and makeup of the feature set. Two approaches to feature selection were studied as was a nearest neighbor classification algorithm based on Mahalanobis distances. Using a pool of ten objects and twelve features, the system was tested by performing studies of hypothetical visual recognition tasks.

1 citations

Journal Article
Liao Qingmin1
TL;DR: A 3D recognition system based on a single projected 2D image to simplify the3D recognition process and a feature selection strategy is developed for recognition of different shapes.
Abstract: Most 3D object recognition methods need too much information for the recognition process so they are not practical for real applications.This paper presents a 3D recognition system based on a single projected 2D image to simplify the 3D recognition process.The system uses zernike moments,trace transformations,and multi-scale autoconvolution for the clustering based viewpoint space partitioning.A shape analysis of the Princeton shape benchmark is used to investigate the recognition of the three features on different 3D shapes.The results show that none of these features is suitable for all kinds of shapes.Therefore,a feature selection strategy is developed for recognition of different shapes.

1 citations

Book ChapterDOI
25 Nov 2020
TL;DR: In this paper, the authors proposed an offline signature recognition system using a multiple algorithm approach using Local Binary Pattern (LBP) and Grey Level Co-occurrence Matrix (GLCM).
Abstract: Signature is a biometric trait that has piqued the interest of researchers. This is due to its high rate of acceptability. Offline signature in particular, has been around for a while and hence its suitability as a biometric trait. This paper proposes an offline signature recognition system using a multiple algorithm approach. The system accepts handwritten signature, filters the signature and crops the signature region. The Local Binary Pattern (LBP) of the signature image is then obtained. After this, Grey Level Co-occurrence Matrix (GLCM) is applied. Statistical features are then extracted. The difference in the stored features and the extracted features was obtained. The output is compared with a threshold for discrimination. This research aims at improving the performance of offline signature recognition using its textural features. The designed system gave an FRR and FAR of 8.6%, 4.6% respectively for MYCT signature database and 8.8%, 5.2% for GPDS signature database.

1 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202310
202219
202122
202028
201925
201832