Advancements in Handwritten English Character Recognition: A Comprehensive Analysis Using Pattern Recognition and Deep Learning on Scanned Documents

The Handwritten English Character Recognition (HECR) system is a vital component that bridges the gap between digital and pen-and-paper processes. Several technologies, including deep learning and pattern recognition, are used to change handwritten English letters in this study. Enhance the accuracy of the identification procedure. The UNIPEN dataset uses state-of-the-art technologies such as U-Net, Convolutional Neural Networks (CNNs), YOLO, and RCNN to identify handwritten characters found in scanned manuscripts. In addition to CNN feature extraction, YOLO and RCNN have many applications in data classification, data collecting, and structured organization. Adjusting the model's hyperparameters is all it takes to make it work better. The results demonstrate the model's efficacy; on testing data, it achieved 99.59% accuracy, and on training data, it achieved 98.44%. The paper highlights the requirement of using numerous deep learning algorithms to improve handwritten character identification, which is a dual aim. Integrating U-Net, CNN, YOLO, and RCNN methodically is driving future technique development.