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.