  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 forged signatures as
distinct classes within an object detection framework, effectively handling
both detection and verification. We employ a DINO-based network augmented with
a dilation module to detect and verify signatures on check images
simultaneously. Our approach achieves an AP of 99.2 for genuine and 99.4 for
forged signatures, a significant improvement over the DINO baseline, which
scored 93.1 and 89.3 for genuine and forged signatures, respectively. This
improvement highlights our dilation module's effectiveness in reducing both
false positives and negatives. Our results demonstrate substantial advancements
in detection-based signature verification technology, offering enhanced
security and efficiency in financial document processing.
