Arabic text detection in videos using neural and boosting-based approaches: Application to video indexing
Text detection in videos is a primary step in any semantic-based video analysis systems. In this work, we propose and compare three machine learning-based methods for embedded Arabic text detection. These methods are able to detect Arabic text regions without any prior knowledge and without any pre-processing. The first method relies on a convolution neural network. The two other methods are based on a multi-exit asymmetric boosting cascade. The proposed methods have been extensively evaluated on a large database of Arabic TV channel videos. Experiments highlight a good detection rate of all methods even though neural network-based method outperforms the other ones in terms of recall/precision and computation time.