Publication Type:

Conference Paper

Source:

2020 5th International Conference on Communication and Electronics Systems (ICCES) (2020)

Keywords:

class wise split data, convolutional neural nets, Convolutional neural network, Convolutional Neural Network (CNN), Deep learning, Digit recognition, Ensemble learning, handwritten character recognition, handwritten digit recognition, image classification, learning (artificial intelligence), Pattern recognition, random split data

Abstract:

In pattern recognition, the recognition of handwritten digits has always been a very challenging and tedious task. In this work, a simple novel approach is proposed to recognize the handwritten digits. The primary goal of this work is recognition of the handwritten digits by using ensemble learning. Ensemble learning improves convergence by decreasing the complexity of the model. The distribution of data in the random split and class-wise split for the base learners has been studied. Detailed analysis of how the load is distributed among the base learners and how it impacts the model accuracy and training time is also discussed. The overall trends of the ensemble model have also been analyzed.

Cite this Research Publication

K. V. P. Nandan, Panda, M., and S. Veni, “Handwritten Digit Recognition Using Ensemble Learning”, in 2020 5th International Conference on Communication and Electronics Systems (ICCES), 2020.