Implementation of a Banknote Watermark Detection Application Leveraging Superior Segmentation Methods

Abstract

Detecting watermarks on banknotes is crucial for verifying authenticity and combating counterfeiting. This study focuses on developing a desktop-based application that leverages OpenCV and PyQt technologies to detect watermarks on banknotes effectively. The application incorporates five advanced segmentation methods: Otsu Thresholding, Adaptive Thresholding, Thresholding, Canny Edge Detection, and K-Means Clustering, aiming to enhance the accuracy of watermark identification. The development process involves digital image processing to extract watermark features and evaluate the performance of each segmentation method based on accuracy and efficiency. Testing results demonstrate that these methods achieve high accuracy in identifying watermarks across various banknote types. This application provides a practical and accessible solution for the public to verify the authenticity of banknotes swiftly and reliably.