Writer Independent Offline Signature Recognition Using Ensemble Learning
This addresses the problem of verifying signatures in realistic, writer-independent scenarios for security applications, but it is incremental as it builds on existing deep learning methods.
The paper tackled offline writer-independent signature verification by proposing an ensemble model using CNNs for feature extraction and RGBT with stacking for classification, achieving state-of-the-art performance on various datasets.
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.