A Bayesian Framework for Fusing Multiple Word Knowledge Models in Videotext Recognition

Videotext recognition is challenging due to low resolution, diverse fonts/styles, and cluttered background. Past methods enhanced recognition by using multiple frame averaging, image interpolation and lexicon correction, but recognition using multi-modality language models has not been explored. In this paper, we present a formal Bayesian framework for videotext recognition by combining multiple knowledge using mixture models, and describe a learning approach based on Expectation-Maximization (EM). In order to handle unseen words, a back-off smoothing approach derived from the Bayesian model is also presented. We exploited a prototype that fuses the model from closed caption and that from the British National Corpus. The model from closed caption is based on a unique time distance distribution model of videotext words and closed caption words. Our method achieves a significant performance gain, with word recognition rate of 76.8% and character recognition rate of 86.7%. The proposed m...

• Type: Conference
• Year: 2003
• Where: CVPR
• Authors: DongQing Zhang, Shih-Fu Chang
• Keywords: Character Recognition Rate, Closed Caption, Computer Vision, CVPR 2003, False Alarm Rate, Methods Enhanced Recognition, Word Recognition Rate