Conference Papers

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

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Author: search.filters.author.Chandrasekaran, K.Subject: search.filters.subject.Convolutional Neural Network

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    Offline Character recognition on Segmented Handwritten Kannada Characters
    (Institute of Electrical and Electronics Engineers Inc., 2019) Joe, K.G.; Savit, M.; Chandrasekaran, K.
    Optical character recognition (OCR) is the conversion of pictures of typed or handwritten characters into machine encoded characters. We chose to work on a subfield of OCR, namely offline learning of handwritten characters. Kannada script is agglutinative, where simple shapes are concatenated horizontally to form words. This paper presents a comparative study between different machine learning and deep learning models on Kannada characters. A Convolutional Neural Network (CNN) was chosen to show that handcrafted features are not required for recognizing classes to which characters belong to. The CNN beats the accuracy score of previous models by 5%. © 2019 IEEE.