Optimizing Handwritten Text Recognition for Automated Note Generation for Enhanced Learning Environment

Handwritten character recognition, pivotal for automating handwritten-to-digital text conversion, is addressed in this study. Employing a feed-forward artificial neural network (ANN) with convolutional neural networks (CNNs) streamlines the process by analysing and categorizing comparable items from diverse image samples, eliminating manual transcription. The system's efficiency is rooted in a hidden-layered feed-forward neural network, adept at discerning unique handwritten character features through extensive training on diverse datasets. Integration of video records enables automated note generation during lectures and events, substantially reducing manual effort for comprehensive records. Experimental results affirm the approach's high accuracy in recognizing handwritten characters, showcasing its potential to revolutionize digitization. This research underscores the prowess of artificial neural networks in complex pattern recognition, contributing significantly to advancing automated handwritten character recognition across domains.