We offer in this article, a method for text extraction in images issued from city scenes. This method is used in the French iTowns project (iTowns ANR project, 2008) to automatically enhance cartographic database by extracting text from geolocalized pictures of town streets. This task is difficult as 1. text in this environment varies in shape, size, color, orientation... 2. pictures may be blurred, as they are taken from a moving vehicle, and text may have perspective deformations, 3. all pictures are taken outside with various objects that can lead to false positives and in unconstrained conditions (especially light varies from one picture to the other). Then, we can not make the assumption on searched text. The only supposition is that text is not handwritten . Our process is based on two main steps: a new segmentation method based on morphological operator and a classification step based on a combination of multiple SVM classifiers. The description of our process is given in this article. The efficiency of each step is measured and the global scheme is illustrated on an example