Handwritten Manuscript

Handwritten manuscript analysis focuses on developing methods for dating, authenticating, and extracting information from historical documents. Current research heavily utilizes machine learning, employing convolutional neural networks, transformer architectures, and Bayesian regression models to analyze handwriting styles, extract key information, and even predict manuscript dates based on stylistic features. These advancements are crucial for historical research, enabling more accurate dating of documents, improved forensic analysis of handwriting, and efficient digitization of large archival collections. The resulting datasets and algorithms are transforming historical scholarship and forensic science.