Automatic text detection using multi-layer color quantization in complex color images
News, magazines, Web pages, etc. in modern life always contain much text information. In this paper, we propose a novel approach to detect text in images with very low false alarm rate. First of all, neural network color quantization is used to compact text color. Second, 3D histogram analysis chooses several color candidates, and then extracts each of these color candidates to obtain several bi-level images. Moreover, for each bi-level image, connectivity analysis and some morphological operators are fed to produce character candidates. Furthermore, we calculate some spatial features and relationships of each text candidate. Finally, we can localize text regions by authentication from LOG (Laplacian of Gaussian) edge detector. Meanwhile, in complex color images, multi-quantization layers can be integrated to reject non-text parts and reduce false alarm rate