Handwriting recognition using Deep Learning in Keras
Shefali Arora, M. P. S. Bhatia · 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) · 2018
Nowadays, deep learning is playing an important role in the domain of image classification. In this paper, a Python library known as Keras, is used for classification of MNIST dataset, a database with images of handwritten images. Two architectures - feed forward neural networks and convolutional neural networks are used for feature extraction and training of model, which is optimized using Stochastic Gradient Descent. This paper gives an overview of multi-class classification of these images using these models, and their performance evaluation in terms of various metrics. It is observed that convolutional neural networks achieve a greater accuracy as compared to feedforward neural networks for classification of handwritten digits.