From Pixels to Predictions Handwritten Digit Recognition using Deep Learning Techniques

Abstract

Abstract: Handwritten digit recognition is a fundamental problem in the field of deep learning, with applications ranging from postal services to finance. In this work, we explore the implementation of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to predict and classify handwritten digits using the MNIST (Modified National Institute of Standards and Technologies) dataset, a well-known collection of handwritten digits. Our primary goal is to determine which of these methods offers higher accuracy in digit recognition. We started by preparing the MNIST dataset. Using this dataset, we construct two separate models: a CNN-based model and an RNN-based model. Both models were trained extensively to learn the intricate patterns and structures within the handwritten digits. This project lets us see which model works better, helping us make smarter choices when we want to predict numbers in different situations

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