This document presents a bottom-up text localization technique that uses document structure features and support vector machines. The technique detects and extracts text from document images through several steps: preprocessing, locating and merging blocks, extracting features from blocks, and using support vector machines trained on the features to classify blocks as containing text or not. The technique uses a flexible feature descriptor based on structural elements that can adapt to different document image types. Experimental results on document images show a 98.5% success rate in classifying blocks.