Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Mulimani, M.Subject: search.filters.subject.Image Processing

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    Currency recognition system using image processing
    (Institute of Electrical and Electronics Engineers Inc., 2017) Abburu, V.; Gupta, S.; Rimitha, S.R.; Mulimani, M.; Koolagudi, S.G.
    In this paper, we propose a system for automated currency recognition using image processing techniques. The proposed method can be used for recognizing both the country or origin as well as the denomination or value of a given banknote. Only paper currencies have been considered. This method works by first identifying the country of origin using certain predefined areas of interest, and then extracting the denomination value using characteristics such as size, color, or text on the note, depending on how much the notes within the same country differ. We have considered 20 of the most traded currencies, as well as their denominations. Our system is able to accurately and quickly identify test notes. © 2017 IEEE.
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    Gender Detection using Handwritten Signatures
    (Institute of Electrical and Electronics Engineers Inc., 2018) Mohit Reddy, J.; Guru Pradeep Reddy, T.; Mishra, S.; Mulimani, M.; Koolagudi, S.G.
    In this paper, a method is proposed which uses both Image Processing and Machine Learning techniques which detects the gender of a person using handwritten signature. A photograph of a handwritten signature is given as input to the model which then extracts different features like pen pressure, slant angle, count external and internal contours etc. The features extracted from multiple images in the dataset are used to train the model, which then predicts the output of a new input given to it. Our objective is to collect unbiased datasets from a set of people and feed those signatures to the model, carrying out the statistical analysis and calculating the accuracy of the algorithm after every signature classification. We have used Adaboost classifier which gave a cross-validation accuracy of 73.2% compared to other classifiers like Gradient Boosting Classifier, Random Forest Trees and Multi-Layer Perceptron which gave 73.2%, 63.2% and 59.6% accuracies respectively. Copy Right © INDIACom-2018.