Enhanced Bank Check Security: Introducing a Novel Dataset and Transformer-Based Approach for Detection and Verification
Muhammad Saif Ullah Khan, Tahira Shehzadi, Rabeya Noor, Didier, Stricker, Muhammad Zeshan Afzal

TL;DR
This paper presents a new dataset and a transformer-based detection approach for bank check signature verification, significantly improving accuracy and robustness in real-world scenarios.
Contribution
Introduces a realistic dataset for check signature verification and a novel object detection method using DINO with a dilation module for improved accuracy.
Findings
Achieved AP of 99.2 for genuine signatures
Achieved AP of 99.4 for forged signatures
Significant improvement over baseline detection methods
Abstract
Automated signature verification on bank checks is critical for fraud prevention and ensuring transaction authenticity. This task is challenging due to the coexistence of signatures with other textual and graphical elements on real-world documents. Verification systems must first detect the signature and then validate its authenticity, a dual challenge often overlooked by current datasets and methodologies focusing only on verification. To address this gap, we introduce a novel dataset specifically designed for signature verification on bank checks. This dataset includes a variety of signature styles embedded within typical check elements, providing a realistic testing ground for advanced detection methods. Moreover, we propose a novel approach for writer-independent signature verification using an object detection network. Our detection-based verification method treats genuine and…
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Taxonomy
TopicsAdvanced Data Processing Techniques
MethodsLinear Layer · Multi-Head Attention · Residual Connection · Softmax · Layer Normalization · Attention Is All You Need · Dense Connections · Vision Transformer · self-DIstillation with NO labels
