SVM vs CNN in Handwritten Digits Classification

Abstract: Image classification has always been one of the most used and highly researched application of machine learning, while it is very easy for us to easily understand and classify the things we see every time. But, for machines to understand and categorize the same things with near human level accuracy, it requires training on large number of images of those objects and lots of internal calculations. Handwritten digits classification is one part of the whole spectrum of the types of images and their categorizations machines are made to do. This research aims to compare the accuracy of a machine learning algorithm i.e. Support Vector Machine (SVM) with that of a deep learning algorithm Convolutional Neural Networks in handwritten digits classification. In this research, we fed in same dataset containing numerous labelled images of handwritten digits to both Support Vector Machine (SVM) and Convulational Neural Network (with 3 CNN layers). The outcome shows that the cnn algorithm outperforms the svm.

Keywords: Image classification, Machine learning, Support Vector Machine (SVM), Convolutional Neural Network (CNN), Handwritten digits classification, Deep learning, Accuracy comparison, Pattern recognition, MNIST dataset, Image recognition.