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Figure from:Fake Currency Detection System

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both genuine and counterfeit currency notes, facilitating thorough evaluation of the detection system's performance.

Figure 3 both genuine and counterfeit currency notes, facilitating thorough evaluation of the detection system's performance.

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The algorithm employed for detecting counterfeit currency involves a multi-step process. Initially, the images undergc reprocessing steps such as grayscale conversion, noise reduction, and normalization to enhance clarity. Following this, feature *xtraction techniques isolate crucial attributes from the currency notes, such as watermarks, security threads, and unique yatterns/colors indicative of genuine notes. These features are then fed into a classification model, potentially utilizing decision trees. Support Vector Machines (SVM), or neural networks, to categorize the notes as genuine or counterfeit. Moreover, the system ncorporates continual model learning, where the algorithm continuously updates through real-time data and incremental learning [his ensures adaptation to evolving counterfeit techniques. Regular performance monitoring and anomaly detection mechanism: acilitate ongoing refinement of the detection system. By employing this iterative approach, the algorithm can effectively identify -ounterfeit currency while maintaining adaptability to emerging counterfeit methods. MATLAB serves as the primary software fot mplementing these processes, leveraging its extensive image processing and machine learning capabilities.
The algorithm employed for detecting counterfeit currency involves a multi-step process. Initially, the images undergc reprocessing steps such as grayscale conversion, noise reduction, and normalization to enhance clarity. Following this, feature *xtraction techniques isolate crucial attributes from the currency notes, such as watermarks, security threads, and unique yatterns/colors indicative of genuine notes. These features are then fed into a classification model, potentially utilizing decision trees. Support Vector Machines (SVM), or neural networks, to categorize the notes as genuine or counterfeit. Moreover, the system ncorporates continual model learning, where the algorithm continuously updates through real-time data and incremental learning [his ensures adaptation to evolving counterfeit techniques. Regular performance monitoring and anomaly detection mechanism: acilitate ongoing refinement of the detection system. By employing this iterative approach, the algorithm can effectively identify -ounterfeit currency while maintaining adaptability to emerging counterfeit methods. MATLAB serves as the primary software fot mplementing these processes, leveraging its extensive image processing and machine learning capabilities.
Figure: 2 Flow Chart for Decision making
Figure: 2 Flow Chart for Decision making
Table 2. Results for 500-2 note The research dataset comprises a diverse assortment of genuine and counterfeit Indian currency notes, meticulously curated to represent various denominations and conditions, thereby offering a comprehensive spectrum for counterfeit detection evaluation. Each image within the dataset undergoes rigorous examination, meticulously addressing torn or heavily soiled regions. This dataset serves as the cornerstone for training and assessing image processing and machine learning algorithms in counterfeit currency detection. By providing the necessary ground truth, it facilitates rigorous performance evaluation and iterative refinement of the detection system, ensuring its robustness and efficacy in real-world applications.
Table 2. Results for 500-2 note The research dataset comprises a diverse assortment of genuine and counterfeit Indian currency notes, meticulously curated to represent various denominations and conditions, thereby offering a comprehensive spectrum for counterfeit detection evaluation. Each image within the dataset undergoes rigorous examination, meticulously addressing torn or heavily soiled regions. This dataset serves as the cornerstone for training and assessing image processing and machine learning algorithms in counterfeit currency detection. By providing the necessary ground truth, it facilitates rigorous performance evaluation and iterative refinement of the detection system, ensuring its robustness and efficacy in real-world applications.
This study offers a comprehensive framework for detecting counterfeit currency, covering crucial aspects such as data cleaning, experimental setup, software configuration, and algorithmic implementation. The data cleaning process involves enhancing image quality and addressing torn or dirty notes, ensuring that captured images are well-prepared for subsequent analysis. In the experimental setup, a digital camera is employed within a controlled lighting environment, complemented by a dataset comprising both genuine and counterfeit currency notes, facilitating thorough evaluation of the detection system's performance.
This study offers a comprehensive framework for detecting counterfeit currency, covering crucial aspects such as data cleaning, experimental setup, software configuration, and algorithmic implementation. The data cleaning process involves enhancing image quality and addressing torn or dirty notes, ensuring that captured images are well-prepared for subsequent analysis. In the experimental setup, a digital camera is employed within a controlled lighting environment, complemented by a dataset comprising both genuine and counterfeit currency notes, facilitating thorough evaluation of the detection system's performance.