Color binarization for complex camera-based images
Cline Thillou, Bernard H. Gosselin · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
This paper describes a new automatic color thresholding based on wavelet denoising and color clustering with K-means in order to segment text information in a camera-based image. A particular focus is given on stroke analysis to improve character segmentation, the step which follows color thresholding. Several parameters bring different information and this paper tries to explain how to use this complementarity. It is mainly based on the discrimination between two kinds of backgrounds: clean or complex. On one hand, this separation is useful to apply a particular algorithm on each of these cases and on the other hand to decrease the computation time for clean cases for which a faster method could be considered. Finally, several experiments were done to discuss results and to conclude that the use of a discrimination between kinds of backgrounds gives better results in terms of Precision and Recall. This separation of backgrounds is done with supervised classification. After tests with several classifiers (linear and quadratic discriminant analysis, K-nearest neighbors, neural networks and support vector machines), best results are given with a set of features based on properties of the gray-level histogram and by using a support vector machine.