A Neuronal Planar Modeling for Handwriting Signature based on Automatic Segmentation
This work addresses signature verification for security applications, but it appears incremental as it builds on existing planar modeling approaches with an automatic segmentation method.
The paper tackled offline handwriting signature verification by proposing a planar neuronal model with automatic segmentation into bands, achieving promising results on two databases including a custom set of 6000 signatures and the public GPDS-300 database with 16200 signatures.
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.