Abstract
News crawl extraction is a subset of media monitoring. It is not directly related to the broadcast itself and not spoken by any anchor or guest - therefore its content cannot be inspected via speech-to-text tools. News crawl content is only available as text in video frames so its extraction can be done using Scene Text Detection techniques. This work tests the most used deep learning Scene Text Detection methods (both regression and semantic segmentation approaches) to see if they can be used as a base in news crawl extraction. Our investigation shows that regression based model CTPN performs better for our particular purpose that the semantic segmentation based EAST and CRAFT models.
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