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20212023
most citedDoc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks

32 citations · 53 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.HC2022★ 3 cited

The RPM3D project: 3D Kinematics for Remote Patient Monitoring

Alicia Fornés, Asma Bensalah, Cristina Carmona-Duarte +9

This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches. We base our analysis on the Kinematic Theory of…

cs.CV2022★ 1 cited

A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts

Giuseppe De Gregorio, Sanket Biswas, Mohamed Ali Souibgui +4

Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available…

cs.CV2022★ 32 cited

Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks

Andrea Gemelli, Sanket Biswas, Enrico Civitelli +2

Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Network…

cs.CV2022

A Generic Image Retrieval Method for Date Estimation of Historical Document Collections

Adrià Molina, Lluis Gomez, Oriol Ramos Terrades +1

Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to othe…

cs.CV2022

Text-DIAE: A Self-Supervised Degradation Invariant Autoencoders for Text Recognition and Document Enhancement

Mohamed Ali Souibgui, Sanket Biswas, Andres Mafla +6

In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text)…

cs.CV2022★ 2 cited

DocEnTr: An End-to-End Document Image Enhancement Transformer

Mohamed Ali Souibgui, Sanket Biswas, Sana Khamekhem Jemni +4

Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for…

cs.CV2022★ 10 cited

DocSegTr: An Instance-Level End-to-End Document Image Segmentation Transformer

Sanket Biswas, Ayan Banerjee, Josep Lladós +1

Understanding documents with rich layouts is an essential step towards information extraction. Business intelligence processes often require the extraction of useful semantic conte…