Handwritten Digits Identification Using Mnist Database Via Machine Learning Models

The identification of hand-written digits is among the most significant issue in the applications for pattern detection. In many application such as postal code, check online routing bank accounts, data form entry, etc., the applications of digits recognition include the center of the issue is the need to construct an appropriate algorithm that can identify hand-written digits and that users upload through a smartphone and scanner and other digital devices. In this paper, we took a repository of MNIST, which is a sub-set of the database of NIST results. The MNIST dataset accommodates the collection of hand-written scanned images from a broader variety of NIST repository produced by hand. The method proposed in this paper is centered on numerous machine learning methods to perform hand-written digit detection that is off-line in the python language platform. The primary objective of this paper is to render hand-written digits recognition reliable and precise. For the identification of digits using MNIST many machine learning algorithms have been used including Support Vector Machine, Multilayer Perceptron, Decision Tree, Naïve Bayes, K-Nearest Neighbor, and Random Forest.