Single-Digit Alphanumeric Recognition System

This project presents an attempt to recognise single, handwritten alphanumeric characters with a machine learning-based approach. Using a dataset of 131,600 handwritten characters, I trained a custom convolutional neural network (CNN), which achieved a 87.94% accuracy rate, and I applied the ResNet-152–a well-known published model, which achieved a 87.13% accuracy rate, for comparison. A full-stack web application was also developed in conjunction with the model for demonstration. These results signify that the depth of the model was not the main culprit for the systematic failure of recognising some pairs of characters, and that a different approach must be attempted.