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.