A comprehensive neural-based approach for text recognition in videos using natural language processing

This work aims at helping multimedia content understanding by deriving benefit from textual clues embedded in digital videos. For this, we developed a complete video Optical Character Recognition system (OCR), specifically adapted to detect and recognize embedded texts in videos. Based on a neural approach, this new method outperforms related work, especially in terms of robustness to style and size variabilities, to background complexity and to low resolution of the image. A language model that drives several steps of the video OCR is also introduced in order to remove ambiguities due to a local letter by letter recognition and to reduce segmentation errors. This approach has been evaluated on a database of French TV news videos and achieves an outstanding character recognition rate of 95%, corresponding to 78% of words correctly recognized, which enables its incorporation into an automatic video indexing and retrieval system.