MNIST Kannada Digit Recognition
Keywords:Computer Vision, Convolutional Neural Network (CNN), Dig MNSIT, Handwritten digit recognition, K-Nearest Neighbor (KNN), Kannada-MNIST, Pattern Recognition, Random Forest Classifier (RFC), Support Vector Machine (SVM)
Handwritten digit recognition, an active research field of computer vision and pattern recognition. This paper proposes an efficient handwritten Kannada digit recognition approach based on comparing the prediction accuracy of the Convolutional Neural Network (CNN) model and various machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest Classifier (RFC) by training on Kannada-MNIST and testing on the Dig MNIST dataset. The Kannada language has complex digits, identifying such digits is a challenge in pattern recognition. In the end, a comparative study of the above-mentioned algorithms is performed based on recognition accuracy.
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Copyright (c) 2021 P. G. Shwetha, Prajna Gatty, Shubhashini, B. Shraddha, K. Janardhana Bhat
This work is licensed under a Creative Commons Attribution 4.0 International License.