Handwritten Text Recognition (HTR) is essential for digitizing historical documents in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 …

We introduce Dessurt, a relatively simple document understanding transformer capable of
being fine-tuned on a greater variety of document tasks than prior methods. It receives a …

Unconstrained handwritten text recognition remains challenging for computer vision
systems. Paragraph text recognition is traditionally achieved by two models: the first one for …

The advent of recurrent neural networks for handwriting recognition marked an important
milestone reaching impressive recognition accuracies despite the great variability that we …

The recognition of handwritten text is challenging as there are virtually infinite ways a human
can write the same message. Deep learning approaches for handwriting analysis have …

Text recognition is a major computer vision task with a big set of associated challenges. One
of those traditional challenges is the coupled nature of text recognition and segmentation …

In this paper, we propose a novel method, aggregation cross-entropy (ACE), for sequence
recognition from a brand new perspective. The ACE loss function exhibits competitive …

Abstract We present a Neural Network based Handwritten Text Recognition (HTR) model
architecture that can be trained to recognize full pages of handwritten or printed text without …

The area of offline handwritten text recognition (OHTR) has been widely researched in the
last decades, but it stills an important research problem. The OHTR system has an objective …

Handwritten Chinese text recognition (HCTR) has been an active research topic for
decades. However, most previous studies solely focus on the recognition of cropped text line …