2 citations · 3 across the 3 of their papers we have counts for
6 papers
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…
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…
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.…
One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition
Mohamed Ali Souibgui, Ali Furkan Biten, Sounak Dey +5
Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models). F…
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…
A Few-shot Learning Approach for Historical Ciphered Manuscript Recognition
Mohamed Ali Souibgui, Alicia Fornés, Yousri Kessentini +1
Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because…