Handwritten Alphabet and Number Recognition with Machine Learning

In this research, a machine learning-based system for handwritten letter and number recognition is implemented. Leveraging a Convolutional Neural Network (CNN) to classify characters accurately, the model is trained using datasets such as EMNIST, which handles a variety of handwriting styles. High recognition accuracy is made possible by the TensorFlow library in Python, which is used for model development and deployment. The application generates text output, anticipates the written characters, and handles input from scanned photographs. This project can be modified to digitize handwritten data for use in corporate or educational institutions, which will improve accessibility and data entry efficiency.