166 citations
- Mohamed Ali Souibgui4 · h 10
- Yousri Kessentini4 · h 19
- A. Ben-Hamadou2 · h 14
- Alicia Forn'es2 · h 10
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- Universitat Autònoma de BarcelonaES3 papers
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- Radboud University Medical CenterNL2 papers
- Radboud University NijmegenNL2 papers
- AGH University of KrakowPL1 paper
- BG Klinikum DuisburgDE1 paper
- Brigham and Women's HospitalUS1 paper
- Center for Applied LinguisticsUS1 paper
- Center for Behavioral Brain SciencesDE1 paper
- Centre Inria de l'Université Grenoble AlpesFR1 paper
- Centre Inria d'Université Côte d'AzurFR1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
10 papers
Interference Management in 5G and Beyond Networks
Nessrine Trabelsi, Lamia Chaari Fourati, Chung Shue Chen
During the last decade, wireless data services have had an incredible impact on people's lives in ways we could never have imagined. The number of mobile devices has increased expo…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge
Achraf Ben-Hamadou, Oussama Smaoui, Ahmed Rekik +29
Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-ba…
Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text Recognition
Mohamed Ali Souibgui, Alicia Fornés, Yousri Kessentini +1
Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. The main difficulty comes from the very few annotated dat…
Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement
Sana Khamekhem Jemni, Mohamed Ali Souibgui, Yousri Kessentini +1
Handwritten document images can be highly affected by degradation for different reasons: Paper ageing, daily-life scenarios (wrinkles, dust, etc.), bad scanning process and so on.…
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement
Mohamed Ali Souibgui, Yousri Kessentini
Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an eff…