Published on Tue Mar 27 2018

A Neuronal Planar Modeling for Handwriting Signature based on Automatic Segmentation

Imen Abroug Ben Abdelghani, Najwa Essoukri Ben Amara

This paper deals with offline handwriting signature verification. We propose an automatic segmentationapproach into bands of signature images. The method has been tested on two databases.

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Abstract

This paper deals with offline handwriting signature verification.We propose a planar neuronal model of signature image. Planarmodelsare generally based on delimiting homogenous zones ofimages; we propose in this paper an automatic segmentationapproach into bands of signature images. Signature image ismodeled by a planar neuronal model with horizontal secondarymodels and a verticalprincipal model. The proposed methodhas been tested on two databases. The first is the one we havecollected; it includes 6000 signaturescorresponding to 60writers. The second is the public GPDS-300 database including16200 signature corresponding to 300 persons. The achievedresults are promising.

Thu May 28 2020
Computer Vision
FCN+RL: A Fully Convolutional Network followed by Refinement Layers to Offline Handwritten Signature Segmentation
Handwritten signature is one of the most reliable biometric methods used by most countries. In the last ten years, the application of technology for verification of handwritten signatures has evolved strongly. The technique used is based on a convolutional encoder-decoder network.
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Research on Offline Handwritten Signature Verification explored a large variety of handcrafted feature extractors. In spite of advancements in the last decades, performance of such systems is still far from optimal. In the GPDS-160 dataset, we obtained an Equal Error Rate of 2.74%.
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Fri May 13 2011
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Planar Pixelations and Image Recognition
Any subset of the plane can be approximated by a set of square pixels. This transition from a shape to its pixelation is rather brutal since it destroys geometric and topological information about the shape. Using a technique inspired by Morse Theory, we algorithmically produce a PL approximation.
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Thu Oct 16 2014
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Kannada script is agglutinative, where simple shapes are concatenated horizontally to form a character. This results in a large number of characters making the task of classification difficult. The use of implicit segmentation technique at the character level resulted in an improvement of around 10%.
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Artificial Intelligence
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Signature verification is an important issue in biometrics. There are many effective methods taking into account dynamics of a signing process. In this paper we propose a new approach to signature partitioning.
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