Text embedded in video sequences is very important to semantic indexing and content-based retrieval system, especially for large scale news collection. However, its detection and extraction is still an open problem due to the variety of its size and the complexity of the backgrounds. In this paper, we propose an approach for automatic Arabic-text localization based on a novel method for text-line detection. On the first stage, we use a line segment detector to detect candidate text lines. Then, we propose a word segment identification algorithm based on specific features for Arabic text in order to remove non-text lines. The last stage concerns the text line estimation and text detection in video frames. Experiment results, that we drove on a large collection of video images issued from news broadcasts show the excellent performance of our approach for text detection with different character sizes, directions and styles even in case of complex image background.
Automatic text detection in video sequences remains a challenging problem due to the variety of sizes, colors and the presence of complex background. In this paper, we attempt to solve this problem by proposing a robust detection-validation schema for text localization in Arabic news video. Candidate text regions are first detected by using a hybrid method which combines MSER detector and edge information. Then, these regions are grouped using morphological operators. Finally, a verification process is applied to remove noisy non-text regions including specific features for Arabic text. Performance and efficacy of the proposed text detection approach have been tested By using Arabic-Text-in-Video database (AcTiV-DB).
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