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- W3137609917 abstract "Advancement of technology in the field of Character Recognition have emerged as the key technology which has helped the entire industrial space in many ways including digitization of hand written manuscripts, postal automation, text to speech conversion to help the blind. Today character recognition has emerged and is available for several languages, through which we are able to recognize character in any particular time, however for some languages for which this technology haven't developed much and needs an improvement in the direction. With development of technologies like Artificial Intelligence, Convolutional Neural Network(CNN) and other Deep Learning Techniques there is a tremendous improvement in performance of Character Recognition system. This paper work presents a comparative evaluation of different machine learning approaches such as Gaussian Naive Bayes, Decision Tree, K-Nearest Neighbor(KNN) Classifier and a deep learning based CNN approach using simple architecture for Devanagari character recognition. The proposed deep learning based CNN model outperformed all other algorithms in terms of accuracy." @default.
- W3137609917 created "2021-03-29" @default.
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- W3137609917 date "2020-11-27" @default.
- W3137609917 modified "2023-10-14" @default.
- W3137609917 title "Performance Evaluation of Learning Based Frameworks for Devanagari Character Recognition" @default.
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- W3137609917 doi "https://doi.org/10.1109/upcon50219.2020.9376420" @default.
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