
Oussama ZayeneInstitute of Artificial Intelligence and Complex Systems · HEIA-FR
Oussama Zayene
Doctor of computer science
About
14
Publications
5,359
Reads
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202
Citations
Introduction
Additional affiliations
March 2018 - March 2018
LATIS. Lab and DIVA group
Position
- Chair
Description
- Workshop on Arabic and derived Script Analysis and Recognition Recognition), London, UK http://asar.ieee.tn
November 2017 - November 2017
LATIS lab., ICosys Institute and DIVA group
Position
- Competition organiser @ICDAR 2017
Description
- Organisation of the 2nd edition of the “AcTiVComp” contest https://diuf.unifr.ch/main/diva/AcTiVComp/index.html
Education
September 2013 - September 2018
University of Sfax & University of Fribourg
Field of study
- Video OCR (Pattern Recognition)
September 2010 - January 2012
National Engineering School of Sousse, Tunisia
Field of study
- Intelligent and Communicating Systems
Publications
Publications (14)
In this paper, we present a semi-automatic news video annotation tool. The tool and its algorithms are dedicated to artificial Arabic text embedded in video news in the form of static text as well as scrolling one. It is performed at two different levels. Including specificities of Arabic script, the tool manages a global level which concerns the e...
After the success of the two first editions of the “Arabic Text in Videos Competition—AcTiVComp”, we are proposing to organize a new edition in conjunction with the 25th International Conference on Pattern Recognition (ICPR’20). The main objective is to contribute in the research field of text detection and recognition in multimedia documents, with...
Dear colleagues who are working on text detection and recognition,
You are strongly invited to participate in the new edition of our competition AcTiVComp: "Arabic text detection and recognition in news videos" within the ICPR2020 conference.
This study presents a novel approach for Arabic video text recognition based on recurrent neural networks. In fact, embedded texts in videos represent a rich source of information for indexing and automatically annotating multimedia documents. However, video text recognition is a non-trivial task due to many challenges like the variability of text...
Recognizing texts in video is more complex than in other environments such as scanned documents. Video texts appear in various colors, unknown fonts and sizes, often affected by compression artifacts and low quality. In contrast to Latin texts, there are no publicly available datasets which cover all aspects of the Arabic Video OCR domain. This pap...
Benchmark datasets and their corresponding evaluation protocols are commonly used by the computer vision community, in a variety of application domains, to assess the performance of existing systems. Even though text detection and recognition in video has seen much progress in recent years, relatively little work has been done to propose standardiz...
The aim of this work is to develop a method for automatic segmentation of the
liver based on a priori knowledge of the image, such as location and shape of
the liver.
We propose in this work an approach for automatic recognition of printed Arabic text in open vocabulary mode and ultra low resolution (72 dpi). This system is based on Hidden Markov Models using the HTK toolkit. The novelty of our work is in the analysis of three complex fonts presenting strong ligatures: DiwaniLetter, DecoTypeNaskh and DecoTypeThu...
We propose in this work an approach for automatic recognition of printed Arabic text in open vocabulary mode and ultra low resolution (72 dpi). This system is based on Hidden Markov Models using the HTK toolkit. The novelty of our work is in the analysis of three complex fonts presenting strong ligatures: DiwaniLetter, DecoTypeNaskh and DecoTypeThu...