Boosting-based approaches for Arabic text detection in news videos
Résumé
In this paper, we propose two boosting-based approaches
for Arabic embedded text detection in news videos.
The first approach uses Multi-Block Local Binary Patterns
features whereas the second one relies on Haar-like features.
Both approaches learn text and non-text classes using a multiexit
asymmetric boosting cascade. Bootstrap has also been used
in order to improve the rejection ability of the classifiers. Text
localization is performed by a sliding window search on a multiscale
pyramid of the input image. The proposed approaches have
been evaluated on a large database of images coming from 4
different Arabic TV channels.