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J. Imaging 2018, 4(2), 32; https://doi.org/10.3390/jimaging4020032

Open Datasets and Tools for Arabic Text Detection and Recognition in News Video Frames

1
LATIS Lab, National Engineering School of Sousse (Eniso), University of Sousse, Sousse 4054, Tunisia
2
DIVA Group, Department of Informatics, University of Fribourg (Unifr), Fribourg 1700, Switzerland
3
ICoSys Institute, HES-SO, University of Applied Sciences, Fribourg 1705, Switzerland
*
Author to whom correspondence should be addressed.
Received: 26 November 2017 / Revised: 23 January 2018 / Accepted: 26 January 2018 / Published: 31 January 2018
(This article belongs to the Special Issue Document Image Processing)
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Abstract

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 paper describes a new well-defined and annotated Arabic-Text-in-Video dataset called AcTiV 2.0. The dataset is dedicated especially to building and evaluating Arabic video text detection and recognition systems. AcTiV 2.0 contains 189 video clips serving as a raw material for creating 4063 key frames for the detection task and 10,415 cropped text images for the recognition task. AcTiV 2.0 is also distributed with its annotation and evaluation tools that are made open-source for standardization and validation purposes. This paper also reports on the evaluation of several systems tested under the proposed detection and recognition protocols. View Full-Text
Keywords: video text detection; video text recognition; AcTiV dataset; Arabic Video OCR video text detection; video text recognition; AcTiV dataset; Arabic Video OCR
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zayene, O.; Masmoudi Touj, S.; Hennebert, J.; Ingold, R.; Essoukri Ben Amara, N. Open Datasets and Tools for Arabic Text Detection and Recognition in News Video Frames. J. Imaging 2018, 4, 32.

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