Offline Handwritten Text

Offline handwritten text recognition and generation are active research areas aiming to automatically process and create handwritten text from images. Current research focuses on improving accuracy and robustness using deep learning models, such as convolutional neural networks (CNNs) combined with recurrent neural networks (RNNs) like LSTMs or Transformers, and diffusion models for generation. These advancements are crucial for applications like digitizing historical documents, improving accessibility for individuals with dysgraphia, and automating tasks involving handwritten data. The development of large, high-quality datasets is also a key focus to further enhance model performance and generalization.