Handwritten Text Recognition (HTR) is essential for digitizing historical documents in
different kinds of archives. In this study, we introduce a hybrid form archive written in French …

In recent years, deep convolutional neural networks have achieved state of the art
performance in various computer vision tasks such as classification, detection or …

Purpose An overview of the current use of handwritten text recognition (HTR) on archival
manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains …

Purpose By mapping-out the capabilities, challenges and limitations of named-entity
recognition (NER), this article aims to synthesise the state of the art of NER in the context of …

The extraction of relevant information carried out by named entities in handwriting
documents is still a challenging task. Unlike traditional information extraction approaches …

Abstract Handwritten Text Recognition is a important requirement in order to make visible
the contents of the myriads of historical documents residing in public and private archives …

This paper presents a systematic literature review of image datasets for document image
analysis, focusing on historical documents, such as handwritten manuscripts and early …

Nowadays, deep learning methods are employed in a broad range of research fields. The
analysis and recognition of historical documents, as we survey in this work, is not an …

This paper describes the Handwritten Text Recognition (HTR) competition on the READ
dataset that has been held in the context of the International Conference on Frontiers in …

Line-level keyword spotting (KWS) is presented on the basis of frame-level word posterior
probabilities. These posteriors are obtained using word graphs derived from the recognition …