In this bachelor thesis a new texture-based approach for the detection of text in digital images is presented. The procedure can be essentially divided into two main tasks, in detection of text blocks and detection of individual words, whereby the individual words are extracted from the detected text blocks. Roughly, the developed method acts with multiple support vector machines, which classify possible text regions of an image into real text regions, using wavelet-based features. In the process the possible text regions are defifined by edge projections with diσerent orientations. The results of the approach are X/Y coordinates, width and height of rectangular regions of an image, which contains individual words. This knowledge can be further processed, for example by an optical character recognition software to get the important and useful text information.