Fake Note Detection of Bangladesh
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This document discusses detecting counterfeit banknotes of Bangladesh through digital image processing techniques. The objectives are to increase security by safeguarding financial systems, provide automation for fast and accurate detection without manual intervention, and reduce costs by using the open-source OpenCV library. The workflow involves image acquisition, pre-processing, feature extraction, and classification using pattern recognition algorithms to identify notes as genuine or fake. Techniques discussed include contour analysis to detect watermarks and microprints, as well as UV/IR detection to verify security features. The document demonstrates the project using tools like PyCharm and libraries like OpenCV, NumPy, and imutils.
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- 1. Detecting Counterfeit Banknotes Of Bangladesh
- 2. Associate Processor CSE,JNU Dr. Md Zulfiker Mahmud Supervised by 2 02
- 3. Agenda Introduction 05 01 Objective 06 02 Workflow for Fake Note Detection 08 04 Steps to Detect Fake Note 09 05 Techniques 10 06 03 Identify Original Note 07 03 Project Demonstration 12 08 Tools and Libraries 11 07
- 4. Team Fix_it Tanvir Ahammed Hridoy ID: B180305020 Susmita Rani Saha B180305047 04
- 5. Introduction Fake currency notes pose a significant threat to financial systems. Counterfeit banknotes and its impact on the economy. Counterfeiting has far-reaching negative effects on various aspects of society, including the economy, consumer trust, public safety, and the rule of law. Digital Image Processing (DIP) techniques can be employed to identify counterfeit banknotes. 05
- 6. Objective Increased Security: Safeguarding financial systems against counterfeit currency. Automation: Fast and accurate detection without manual intervention. Cost-Efficient: OpenCV is an open-source library, reducing the need for expensive proprietary software. 06
- 8. Workflow for Fake Note Detection Feature Extraction Classification Acquisition Pre-processing Image enhancement techniques to improve the quality of the captured image. Scanning or capturing the image of the banknote. Extracting relevant features from the pre-processed image. Use Pattern recognition algorithms to classify the note as genuine or fake 08
- 9. Steps to Detect Fake Note 09
- 10. Techniques in Fake Note Detection: Contour Analysis: Contour Analysis can also be used in watermark detection and microprint analysis. Watermark Detection: Watermarks are embedded security features in genuine banknotes that are often subtle and difficult to replicate. By analyzing the contours and regions of interest, contour detection can help identify the presence and location of watermarks. Microprint Analysis: Microprint is a security feature consisting of tiny, intricate patterns that are difficult to replicate accurately. By extracting the contours of microprint regions, contour detection can assist in identifying and comparing the microprint patterns on the banknote, aiding in determining its authenticity. UV/IR Detection: Verifying the presence of ultraviolet (UV) or infrared (IR) features on genuine notes. 10
- 11. Tools and Libraries Tools: PyCharm IDE Libraries: OpenCV :(Open Source Computer Vision Library) is a popular open-source library for computer vision and image processing tasks. numpy : This is a popular library for numerical computations in Python. It provides support for multi-dimensional arrays and mathematical functions. imutils: This is a package that provides convenience functions to simplify common image processing tasks, such as resizing, rotating, and contour operations. 11