Sequential Monte Carlo video text segmentation

This paper presents a probabilistic algorithm for segmenting and recognizing text embedded in video sequences. The algorithm approximates the posterior distribution of segmentation thresholds of video text by a set of weighted samples. After initialization the set of samples is recursively refined by random sampling under a temporal Bayesian framework. The proposed methodology allows us to estimate the optimal text segmentation parameters directly in function of the string recognition results instead of segmentation quality. Results on a database of 6944 images demonstrate the validity of the algorithm. 1

• Type: Conference
• Year: 2003
• Where: ICIP
• Authors: Datong Chen, Jean-Marc Odobez
• Keywords: ICIP 2003, Image Processing, Optimal Text Segmentation, Probabilistic Algorithm, Segmentation Quality, Segmentation Thresholds, Temporal Bayesian Framework