图谱理论在文本图像二值化算法中的应用

Application of graph spectral theory to text image binarization processing

常用的阈值二值化方法不能很有效地分割出文本图像,而利用图谱理论的思想可以清晰有效地对文本图像进行二值化分割。针对传统的图谱理论分割图像算法计算量大、空间复杂度高的不足,提出了利用直方图灰度等级代替像素级,在此基础上近似计算了权函数的参数,算法的计算量和复杂度都有所降低。实验结果表明,该方法大大降低了计算的复杂性,在速度上优于传统的图谱理论分割方法,质量上优于常用的二值化分割方法。

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 resuhs 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.