Key-text spotting in documentary videos using Adaboost

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.