Kannada handwritten numeral recognition through deep learning and optimized hyperparameter tuning

Abstrak

The classification of handwritten numerals is a vital and challenging task in developing automated systems, including postal address sorting and license plate recognition. The present study elucidates a new methodology for recognizing Kannada handwritten numerals using deep learning ResNet and VGG architecture with transfer learning. The challenge in Kannada handwritten recognition is complicated structural hierarchy and large vocabulary. The major problem in deep neural networks is vanishing gradient, which can lead to degradation in character recognition, and was addressed using our new methodology using ResNet architecture. We apply the proposed ResNet method in various real-world applications and compare it with convolutional neural networks (CNN) architecture, VGG. The experiment was implemented with the Google Colab software version on a self-created dataset, with handwritten Kannada numerals fed as the input to the recognition process. Our proposed method achieved a high accuracy of 99.20% on training samples and a generalization accuracy of 97.5% on test samples, indicating our method's effectiveness in recognizing handwritten Kannada numerals.