Own Handwritten Digit recognition using MLP and CNN in tensorflow
Deepti Nikumbh, Rupali Santosh Kale
Object recognition in image is very popular and is widely used in almost all image processing applications. Handwritten digit recognition system is one such application. This paper presents an approach on developing a handwritten digit recognition system using multi-layer neural network and Convolutional Neural Network. These neural network models are trained and tested using the MNIST dataset. Further a real time dataset of authors own handwritten digit where used to test the performance of the system , a comparison of two deep learning models in terms of accuracy i.e successfully classifying digits between 0-9 and computational time taken is presented. The neural network models are developed in python using tensorflow a machine learning library.