Design and Implementation of Financial Bill Anti Counterfeiting and Recognition System based on Computer Vision

In the context of rapid digitalization, financial bills play a critical role in economic activities, yet traditional anti-counterfeiting methods face challenges due to increasingly sophisticated forgery techniques such as high-precision printing and image manipulation software. This paper presents a computer vision-based financial bill anti-counterfeiting and recognition system, integrating advanced image preprocessing (including grayscale conversion, median-Gaussian denoising, and edge detection for cropping), feature extraction using local binary patterns (LBP) and scale-invariant feature transform (SIFT), and deep learning models to detect security features (watermarks, microtext, encrypted QR codes) and extract key information (amounts, dates). The modular system architecture includes image acquisition, preprocessing, feature extraction, authenticity verification, information recognition, and result output. Experimental validation on 100 samples demonstrates the system's effectiveness: 98% accuracy in anti-counterfeiting detection, 100% security and confidentiality via encrypted storage and blockchain integration, and over 99% precision in recognizing critical bill data. This innovative approach addresses limitations of conventional methods, offering a reliable solution for financial management. Future work will focus on enhancing low-quality image preprocessing and exploring blockchain integration for full lifecycle traceability, further improving system robustness and security.