145 citations · 432 across the 15 of their papers we have counts for
14 papers · 1 filter
Faster DAN: Multi-target Queries with Document Positional Encoding for End-to-end Handwritten Document Recognition
Denis Coquenet, Clément Chatelain, Thierry Paquet
Recent advances in handwritten text recognition enabled to recognize whole documents in an end-to-end way: the Document Attention Network (DAN) recognizes the characters one after…
Confidence Estimation for Object Detection in Document Images
Mélodie Boillet, Christopher Kermorvant, Thierry Paquet
Deep neural networks are becoming increasingly powerful and large and always require more labelled data to be trained. However, since annotating data is time-consuming, it is now n…
Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods
Mélodie Boillet, Christopher Kermorvant, Thierry Paquet
Text line segmentation is one of the key steps in historical document understanding. It is challenging due to the variety of fonts, contents, writing styles and the quality of docu…
DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition
Denis Coquenet, Clément Chatelain, Thierry Paquet
Unconstrained handwritten text recognition is a challenging computer vision task. It is traditionally handled by a two-step approach, combining line segmentation followed by text l…
Including Keyword Position in Image-based Models for Act Segmentation of Historical Registers
Mélodie Boillet, Martin Maarand, Thierry Paquet +1
The segmentation of complex images into semantic regions has seen a growing interest these last years with the advent of Deep Learning. Until recently, most existing methods for Hi…
SPAN: a Simple Predict & Align Network for Handwritten Paragraph Recognition
Denis Coquenet, Clément Chatelain, Thierry Paquet
Unconstrained handwriting recognition is an essential task in document analysis. It is usually carried out in two steps. First, the document is segmented into text lines. Second, a…
Recurrence-free unconstrained handwritten text recognition using gated fully convolutional network
Denis Coquenet, Clément Chatelain, Thierry Paquet
Unconstrained handwritten text recognition is a major step in most document analysis tasks. This is generally processed by deep recurrent neural networks and more specifically with…
Have convolutions already made recurrence obsolete for unconstrained handwritten text recognition ?
Denis Coquenet, Yann Soullard, Clément Chatelain +1
Unconstrained handwritten text recognition remains an important challenge for deep neural networks. These last years, recurrent networks and more specifically Long Short-Term Memor…
Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks
Mélodie Boillet, Christopher Kermorvant, Thierry Paquet
In this paper, we introduce a fully convolutional network for the document layout analysis task. While state-of-the-art methods are using models pre-trained on natural scene images…
End-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention Network
Denis Coquenet, Clément Chatelain, Thierry Paquet
Unconstrained handwritten text recognition remains challenging for computer vision systems. Paragraph text recognition is traditionally achieved by two models: the first one for li…
A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units
Wassim Swaileh, Yann Soullard, Thierry Paquet
We address the design of a unified multilingual system for handwriting recognition. Most of multi- lingual systems rests on specialized models that are trained on a single language…
A syllable based model for handwriting recognition
Wassim Swaileh, Thierry Paquet
In this paper, we introduce a new modeling approach of texts for handwriting recognition based on syllables. We propose a supervised syllabification approach for the French and Eng…
LV-ROVER: Lexicon Verified Recognizer Output Voting Error Reduction
Bruno Stuner, Clément Chatelain, Thierry Paquet
Offline handwritten text line recognition is a hard task that requires both an efficient optical character recognizer and language model. Handwriting recognition state of the art m…
Handwriting recognition using Cohort of LSTM and lexicon verification with extremely large lexicon
Bruno Stuner, Clément Chatelain, Thierry Paquet
State-of-the-art methods for handwriting recognition are based on Long Short Term Memory (LSTM) recurrent neural networks (RNN), which now provides very impressive character recogn…