Handwritten Text Recognition

Handwritten text recognition (HTR) aims to automatically transcribe handwritten text from images, a challenging task due to variations in handwriting styles and document conditions. Current research focuses on improving accuracy and efficiency using deep learning architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers, often incorporating techniques such as attention mechanisms, transfer learning, and self-supervised learning to address data scarcity. Advances in HTR have significant implications for digitizing historical archives, improving accessibility to historical documents, and enabling large-scale analysis of handwritten data across various languages and scripts.