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Proceedings Paper

Key-text spotting in documentary videos using Adaboost
Author(s): M. Lalonde; L. Gagnon
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Paper Abstract

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.

Paper Details

Date Published: 17 February 2006
PDF: 8 pages
Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60641N (17 February 2006); doi: 10.1117/12.641924
Show Author Affiliations
M. Lalonde, Computer Research Institute of Montreal (Canada)
L. Gagnon, Computer Research Institute of Montreal (Canada)

Published in SPIE Proceedings Vol. 6064:
Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning
Nasser M. Nasrabadi; Edward R. Dougherty; Jaakko T. Astola; Syed A. Rizvi; Karen O. Egiazarian, Editor(s)

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