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Handwritten Signature Verification Is One Of The Most Widely Used Biometric Authentication Methods Due To Its Simplicity, Uniqueness, And Acceptance In Legal And Financial Transactions. However, Traditional Manual Verification Methods Are Prone To Human Error, Time-consuming, And Susceptible To Forgery. With The Rapid Advancement Of Computer Vision And Pattern Recognition, Automated Signature Verification Has Become A Promising Solution For Enhancing Security And Reliability. This Study Presents A Handwritten Signature Verification System Utilizing OpenCV Techniques For Image Preprocessing, Feature Extraction, And Classification. The System Employs Image Enhancement Methods Such As Grayscale Conversion, Thresholding, Edge Detection, And Contour Analysis To Extract Discriminative Features From Signatures. These Features Are Then Compared Using Similarity Measures Or Machine Learning Models To Distinguish Between Genuine And Forged Signatures. The Proposed Approach Demonstrates The Effectiveness Of OpenCV-based Techniques In Achieving Accurate, Efficient, And Scalable Signature Verification, Offering Potential Applications In Banking, Legal Documentation, And Secure Identity Management.