Application of graph spectral theory to text image binarization processing

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The traditional binarization thresholding methods cannot segment the text image effectively from the whole image,while the improved method based on graph spectral theory can segment the text image effectively and clearly.Concerning the traditional algorithms based on the graph spectral theory has high computational and space complexity,the authors used gray levels of an image instead of pixels of an image.On this basis,the parameters of weight function was calculated approximately.The experimental results show that this method reduces the computational complexity,and has superior performance on speed compared to the traditional graph spectral methods,and better quality compared to the common binarization algorithms.