Detection and Recognition of Arabic Text in Video Frames

Wataru Ohyama, Seiya Iwata, Tetsushi Wakabayashi, Fumitaka Kimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


The authors have developed an end-to-end system for Arabic text recognition in video frames. The end-to-end system consists of the steps for text-line detection, word segmentation and word recognition. In order to achieve high text recognition accuracy we propose a new scheme of integrated text detection-recognition scheme, where the true text-lines are detected with as higher recall rate as possible and the false words in the false lines are rejected in the successive word recognition step. We reported a recognition based transition frame detection of Arabic news captions in single channel video images. In this paper the recognition system is integrated with n-gram language model and extended to text detection/recognition of multi-channel video images. The multi-channel, multi-font performance of the system is experimentally evaluated using AcTiV-D and AcTiV-R dataset. The multi-channel text detection performance for three channels, France24, Russia Today and TunisiaNat1 is 91.29% in (F)-measure. The multi-channel, multi-font character recognition performance for these channels is 94.84% in F-measure.

Original languageEnglish
Title of host publicationProceedings - 6th International Workshop on Multilingual OCR, MOCR 2017
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538635865
Publication statusPublished - Jul 2 2017
Externally publishedYes
Event6th International Workshop on Multilingual OCR, MOCR 2017 - Kyoto, Japan
Duration: Nov 11 2017 → …

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
ISSN (Print)1520-5363


Other6th International Workshop on Multilingual OCR, MOCR 2017
Period11/11/17 → …

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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