Detecting Persian Signatures in Realistic Images using the YOLO Algorithm
Shiva Jafarian, Elham Shabaninia, Hossein Nezamabadi–pour · 2024
While crucial for document forensics and security, detecting Persian signatures in real-world scenarios poses a considerable challenge due to the distinctive features of Persian signatures—characterized by complex combinations of shapes and letters instead of conventional names—along with issues like noise, cluttered backgrounds, and more. Furthermore, the scarcity of annotated datasets complicates signature detection in natural settings. This paper addresses these challenges by introducing a new dataset for Persian documents, merging publicly available signatures with diverse backgrounds to create semi-realistic images reflecting real-world conditions. Additionally, the proposed method leverages the YOLOv5 architecture for signature detection. The experimental findings illustrate that, under various lighting conditions, background complexities, and signature distortions, this approach effectively and accurately detects Persian signatures.