Handwritten Document Recognition

Handwritten document recognition (HDR) aims to automatically transcribe and understand handwritten text, encompassing tasks like layout analysis, text recognition, and named entity recognition. Current research heavily emphasizes end-to-end models, often employing convolutional neural networks (CNNs) for feature extraction and transformer-based architectures for sequential processing, moving away from traditional multi-step approaches. This field is significant for its potential to automate the digitization of historical archives and other large collections of handwritten documents, improving accessibility and enabling large-scale text analysis. Furthermore, advancements in HDR contribute to broader progress in areas like machine learning and computer vision.