Abstract: A method for unsupervised text location in color image is presented.The method makes a brightness grading in R、G、B color layers of a color image separately to avoid choosing the number of clustering in common methods witch based on color clustering，and decreases the complexity of the background.Considering obvious directionality and color stability of text strokes，a rough text location is proceeded according algorithm of orientation gradient.And then，precisely discriminating is implemented with a multi-class SVM classifier.The new method constrains the types of candidate areas and depressed the difficulty of training SVM classifier.Those make the new method higher accuracy and robustness.

摘要： 提出一种彩色图像下的文本提取方法，该方法对彩色图像在R、G、B三个颜色层分别进行亮度分级，以避开传统颜色聚类方法的聚类数目选择问题，降低图像复杂度；考虑到文字笔画的显著方向性特征，并且通常具有稳定的颜色，利用方向梯度算法进行文本粗定位；然后进一步利用多类SVM分类器实现文本区域精确判别。新方法限制了候选区域的种类，从而降低了SVM分类器的训练难度，具有较高的准确性和鲁棒性。