TY - GEN
T1 - Detection and Recognition of Arabic Text in Video Frames
AU - Ohyama, Wataru
AU - Iwata, Seiya
AU - Wakabayashi, Tetsushi
AU - Kimura, Fumitaka
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/1/25
Y1 - 2018/1/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85045270009&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045270009&partnerID=8YFLogxK
U2 - 10.1109/ICDAR.2017.360
DO - 10.1109/ICDAR.2017.360
M3 - Conference contribution
AN - SCOPUS:85045270009
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 20
EP - 24
BT - Proceedings - 6th International Workshop on Multilingual OCR, MOCR 2017
PB - IEEE Computer Society
T2 - 6th International Workshop on Multilingual OCR, MOCR 2017
Y2 - 11 November 2017
ER -