A Hierarchical Method for Kannada-MNIST Classification Based on Convolutional Neural Networks
Ali Beikmohammadi, Najmeh Zahabi · 2021
Handwritten digit classification considers one of the crucial subjects in machine vision due to its numerous practical usages in many recognition systems. In this regard, Kannada-MNIST was introduced as a challenging dataset. On the other hand, deep neural networks, especially convolutional neural networks, give us an encouraging promise to solve such a problem. In this paper, as a result, we propose a new hierarchically combination method with the help of two CNN models designed from scratch. The results of this novel approach on the Kannada-MNIST dataset indicate its excellent performance because the accuracy on the training, validation, and test sets are 99.86%, 99.66%, and 99.80%, respectively. Fortunately, this proposed method has been able to overcome all the state-of-the-art solutions with the best performance on this dataset.