Caption Text Recognition in Video Frames by MAP Matching
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Performance Improvements to the BBN Byblos OCR System
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
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Abstract: In this paper we present a method for performing Optical Character Recognition (OCR) of text in video images. Recognition of videotext is a challenging problem due to various factors such as the presence of rich, dynamic backgrounds, low resolution, color, etc. Our strategy is to process the video images to produce high-resolution binarized text images that resemble printed text. We describe a novel clustering and relaxation procedure that combines stroke and color information to separate the text from the background. The binarized text image is then recognized with our Byblos OCR engine [5][6] using hidden Markov models trained on similar data. We present experimental results on a video-data corpus collected from broadcast news programs. Currently the system delivers a character error rate of 8.3% on independent multi-font test data from this corpus.