MLLP researcher Ihab Khoury earned today his PhD after defending his dissertation

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Ihab khoury receives the congratulations of his advisors and the audience at his PhD defence after the announcement of his success by the PhD examination committee.

MLLP researcher Ihab Khoury defended successfully today his PhD thesis on “Arabic Text Recognition and Machine Translation at the Universitat Politècnica de València’s Department of Computer Systems and Computation.

Ihab Khoury‘s advisors for his PhD thesis were Dr. Alfons Juan Ciscar and Dr. Jesús Andrés Ferrer.

Some of the most significant results covered in Mr. Khoury’s PhD thesis have been featured in publications including articles for Elsevier’s Pattern Recognition Letters (vol. 51, 2015; vol. 35, 2014), for the International Conference on Image Analysis and Processing 2013, and competitions such as NIST OpenHart 2013 and ICDAR 2013, where the system presented was ranked 2nd with very competitive results.

The examining committee for the PhD defence was made up of Prof. Dr. Laurent Heutte (Univ. de Rouen), Dr. Ismael García Varea (Univ. de Castilla-La Mancha) and Dr. Jorge Civera Saiz (Univ. Politècnica de València). They awarded Mr Khoury’s dissertation the qualification of Excellent summa cum laude (the summa cum laude mention having been awarded after a secret vote by the examining committee members, as required by Spanish academic regulations).

After his successful PhD defence, Mr. Khoury has earned the title “Doctor en Informàtica” (PhD in Computer Science) according to the Valencian and Spanish university systems.

Dr. Khoury’s PhD dissertation can be read here at the UPV’s institutional repository Riunet. You can also read the dissertation’s abstract here (in English, Arabic, Catalan and Spanish). Or find other publications by Dr. Khoury on Riunet.

Congratulations to Dr. Khoury on his new title!

Update (14 July 2015): This article has been updated to include a direct link to Dr. Ihab Khoury’s PhD dissertation after its publication by the UPV.

Picture source: MLLP

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