A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Pattern Spectrum and Multiscale Shape Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of pattern recognition & computer vision
Document Image Binarization Based on Texture Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Granulometries and opening trees
Fundamenta Informaticae - Special issue on mathematical morphology
Spatial Size Distributions: Applications to Shape and Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical color models with application to skin detection
International Journal of Computer Vision
Spatial and Feature Space Clustering: Applications in Image Analysis
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Classification of binary textures using the 1-D Boolean model
IEEE Transactions on Image Processing
Text extraction using component analysis and neuro-fuzzy classification on complex backgrounds
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
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In this paper, a novel binarization algorithm for color text images is presented. This algorithm effectively integrates color clustering and binary texture analysis, and is capable of handling situations with complex backgrounds. In this algorithm, dimensionality reduction and graph theoretical clustering are first employed. As the result, binary images related to clusters can be obtained. Binary texture analysis is then performed on each candidate binary image. Two kinds of effective texture features, run-length histogram and spatial-size distribution related respectively, are extracted and explored. Cooperating with an LDA classifier, the optimal candidate of the best binarization effect is obtained. Experiments with images collected from Internet has been carried out and compared with existing techniques, both show the effectiveness of the algorithm.