A Comprehensive Neural-Based Approach for Text Recognition in Videos using Natural Language Processing
Résumé
This work aims at helping multimedia content understanding by deriving bene t from textual clues embedded in digital videos. For this, we developed a complete video Optical Character Recognition system (OCR), speci cally 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.
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