MNIST Digit Classification

Keywords: MNIST Digit

Background

Digit Classification is a method of training the DL models to learn the pattern corresponding to each handwritten digit. The Digit Classification is one of the very important applications of Deep Learning (DL) architecture and finds application in a variety of sectors and industries. In the sector of postal services, digit classification models are used to convert handwritten digits like ZIP codes into digital form, reading the digits on bank cheque slips and numerical entries made by hand. Therefore it becomes important to correctly classify the digits. Different people write the digit in different styles and therefore there is high variability in hand writings and also presence of noise can make the digit classification task a challenging one. Often deep learning architectures like CNN are employed for the tasks related to Images. However, in this study, the author develops the classification model using the ANN.

Aim & Objectives

Aim
To Classify MNIST Digit Using DL Technique
Objective
Develop an effective Artificial Neural Network (ANN) classification model for digit recognition, addressing the challenge of varying handwriting styles and noise, in order to enhance accuracy and enable efficient conversion of handwritten digits into digital form, particularly in sectors like postal services and banking.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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