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17 February 2006 Key-text spotting in documentary videos using Adaboost
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This paper presents a method for spotting key-text in videos, based on a cascade of classifiers trained with Adaboost. The video is first reduced to a set of key-frames. Each key-frame is then analyzed for its text content. Text spotting is performed by scanning the image with a variable-size window (to account for scale) within which simple features (mean/variance of grayscale values and x/y derivatives) are extracted in various sub-areas. Training builds classifiers using the most discriminant spatial combinations of features for text detection. The text-spotting module outputs a decision map of the size of the input key-frame showing regions of interest that may contain text suitable for recognition by an OCR system. Performance is measured against a dataset of 147 key-frames extracted from 22 documentary films of the National Film Board (NFB) of Canada. A detection rate of 97% is obtained with relatively few false alarms.
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M. Lalonde and L. Gagnon "Key-text spotting in documentary videos using Adaboost", Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60641N (17 February 2006);


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