HTR System

Handwritten text recognition (HTR) systems aim to automatically transcribe handwritten text, a challenging task due to variations in writing styles and image quality. Current research focuses on improving accuracy and efficiency using deep learning architectures, particularly convolutional neural networks (CNNs) combined with recurrent neural networks (RNNs) like LSTMs, and increasingly, vision transformers (ViTs). These advancements are driven by the need for robust and adaptable systems, addressing issues like limited training data and writer variability through techniques such as transfer learning, meta-learning, and data augmentation. The resulting improvements have significant implications for digitizing historical archives, automating data entry, and enhancing accessibility for individuals with disabilities.