A Comparative Study of Different Deep Learning Model for Recognition of Handwriting Digits

With the expansion of Artificial Neural Network (ANN), Deep Learning (DL) has brought interesting turn in the various fields of Artificial Intelligence (AI) by making it smarter and more efficient than what we had even in 10-2 years back. DL has been in use in various fields due to its versatility. Convolutional Neural Network (CNN) is at the major point of advancement that brings together the ANN and innovative DL techniques. In this research paper, we have contrived a multi-layer, fully connected neural network (NN) with 10 and 12 hidden layers for handwritten digits (HD) recognition. The testing is performed on the publicly attainable MNIST handwritten database. We selected 60,000 images from the MNIST database for training, and 10,000 images for testing. Our multi-layers ANN (10), ANN (12) and CNN are able to achieve an overall accuracy of 99.10%, 99. 34% and 99.70% respectively while determining digits using the MNIST handwriting dataset.