Pattern recognition is the process of classifying input data into objects, classes, or categories using computer algorithms based on key features or regularities. Pattern recognition has applications in computer vision, image segmentation, object detection, radar processing, speech recognition, and text classification, among others. Analysis on Indian Currency Recognition” 2014 5th International Conference on Computer and Communication Technology 978-1-4799-6758-2/14/$31.00 ©2014 IEEE 291, pp.291-296, 2014 15 M.Deborah and Soniya Prathap “Detection of Fake currency using Image Processing”. IJISETInternational Journal of Innovative Science, Engineering &. Matlab Code for Fake Currency Recognition Using Image Processing IEEE Project. Roshan Helonde 09:18. The Reserve Bank is the one which issue bank notes in India. Reserve Bank, changes the design of bank notes from time to time. Reserve bank uses several techniques to detect fake currency.
You can apply machine learning or deep learning techniques with MATLAB® in pattern recognition applications.
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A machine learning approach consists of preparing your data, manually extracting features to differentiate between classes in the data, and training a machine learning model to classify new objects. Common machine learning techniques or models for object detection include aggregate channel features (ACF), SVM classification using histograms of oriented gradient (HOG) features, and Viola-Jones. These methods are all available in MATLAB®.
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A deep learning approach consists of preparing your data and training the deep neural net, and testing the trained model on new data. Common deep learning models used for pattern recognition are R-CNN and YOLO v2, which are also available in MATLAB. In recent years, deep learning approaches have become more popular than machine learning ones.
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The main differences between machine learning and deep learning approaches are that deep learning models require a larger training dataset and more training time, whereas machine learning models can be trained with a smaller dataset, may be easier to interpret and debug if not working as expected, but yield lower accuracy than a deep learning model trained on a large set of labelled data.