Title: Writer Independent Offline Signature Recognition Using Ensemble Learning,
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, the dynamic information of the signature writing
process is lost, and it is difficult to design good feature extractors that can
distinguish genuine signatures and skilled forgeries. This verification task is
even harder in writer independent scenarios which is undeniably fiscal for
realistic cases. In this paper, we have proposed an Ensemble model for offline
writer, independent signature verification task with Deep learning. We have
used two CNNs for feature extraction, after that RGBT for classification &
Stacking to generate final prediction vector. We have done extensive
experiments on various datasets from various sources to maintain a variance in
the dataset. We have achieved the state of the art performance on various
datasets.