Handwritten Text

Handwritten text recognition and analysis is a vibrant research area aiming to automatically interpret and utilize handwritten documents, encompassing tasks like transcription, writer identification, and even predicting characteristics like BMI from handwriting style. Current research heavily employs deep learning models, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs), often combined with techniques like self-supervised learning and attention mechanisms to improve accuracy and robustness. This field is crucial for digitizing historical archives, improving accessibility for individuals with disabilities, automating administrative tasks, and advancing forensic science applications.