Deep Learning for Handwritten Digit Recognition

Handwritten digit recognition plays a crucial role in applications like automated form processing and character recognition software. This study explores how well the traditional K-Nearest Neighbors (KNN) algorithm compares to a Convolutional Neural Network (CNN) in terms of accuracy and processing efficiency for recognizing handwritten numbers. Materials and Methods: This study used the MNIST dataset, which consists of 70,000 images of handwritten digits. Participants were divided into two groups. Group 1 used the K-Nearest Neighbors (KNN) algorithm, training it over multiple epochs with techniques like batch normalization and dropout to prevent overfitting. The focus was on optimizing KNN for image recognition tasks. Group 2 worked with Convolutional Neural Networks (CNNs), experimenting with different K values to determine the best configuration. The models were evaluated based on scalability, accuracy, and training time. For statistical analysis, a power level of 80% was used with a 0.05 significance threshold and a 95% confidence interval. Results: Compared to the K-NN model (Group 2), which recorded an accuracy of 95% to 97%, the CNN model (Group 1) achieved superior accuracy, ranging from 98% to 99%. CNN was also more effective for real-world applications. The optimal test accuracy for handwritten digit recognition using CNN was 98.4%, with a significance value of 0.0048 compared to KNN's maximum accuracy of 96.8%. Conclusion: CNN is a more dependable and efficient method for handwritten digit recognition than K-NN because it performed better in terms of accuracy, processing speed, and scalability. CNN's exceptional performance makes it ideal for applications like intelligent character recognition systems and automated document processing that demand fast and precise digit recognition.