Writer Independent Offline Signature Recognition Using Ensemble Learning

Signature (topology) Ensemble Learning Online and offline
DOI: 10.48550/arxiv.1901.06494 Publication Date: 2019-01-01
ABSTRACT
The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. In offline (static) signature verification, dynamic information writing process is lost, and it difficult to design good feature extractors that can distinguish genuine signatures skilled forgeries. This verification task even harder writer independent scenarios which undeniably fiscal for realistic cases. this paper, we have proposed Ensemble model writer, with Deep learning. We used two CNNs extraction, after RGBT classification & Stacking generate final prediction vector. done extensive experiments on various datasets from sources maintain a variance dataset. achieved state art performance datasets.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES ()
CITATIONS ()
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....