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Improving Automated Grading Systems: A Comparison Of Machine Learning Techniques For Recognising Handwritten Digits

Author(s): Pranati Mishra*, Sarans Mishra, Kalinga Kumar Khatua, Meenakshi Kandpal, Jyotirmayee Rautray and Ranjan Kumar Dash

Pp: 172-190 (19)

DOI: 10.2174/9798898814717126010014

* (Excluding Mailing and Handling)

Abstract

The paper focuses on handwritten digit detection using the MNIST 0-9 dataset to develop an automated grading system. Four distinct machine learning models are employed: basic and latest Neural Network Models, such as Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN), as well as traditional models like K-Nearest Neighbors (KNN) and Gradient Boosting (XGBoost). The objective is to accurately identify handwritten digits, which is crucial for grading purposes. By evaluating the models' performance metrics, the aim is to determine the most suitable model for efficient digit detection. The ultimate goal is to streamline the grading process, ensuring timely result declaration while maintaining proper record maintenance.

Keywords: CNN, Handwritten digit detection, KNN, MLP, MNIST, Performance metrics, Xgboost.

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