Joint Subtitle Extraction and Frame Inpainting for Videos with Burned-In Subtitles

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Abstract
Subtitles are crucial for video content understanding. However, a large amount of videos have only burned-in, hardcoded subtitles that prevent video re-editing, translation, etc. In this paper, we construct a deep-learning-based system for the inverse conversion of a burned-in subtitle video to a subtitle file and an inpainted video, by coupling three deep neural networks (CTPN, CRNN, and EdgeConnect). We evaluated the performance of the proposed method and found that the deep learning method achieved high-precision separation of the subtitles and video frames and significantly improved the video inpainting results compared to the existing methods. This research fills a gap in the application of deep learning to burned-in subtitle video reconstruction and is expected to be widely applied in the reconstruction and re-editing of videos with subtitles, advertisements, logos, and other occlusions.
Funding Information
  • National Natural Science Foundation of China (61907025, 61702278)
  • Natural Science Foundation of Jiangsu Higher Education Institutions of China (19KJB520048)
  • NUPTSF (NY219069)

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