The development of document image databases is challenging document image retrieval techniques. Traditional layout reconstructed-based methods rely on high quality document images and can only deal with several widely used languages. The complexity of document layouts greatly hinter layout analysis-based approaches. This paper describes a multi-density feature-based algorithm for binary document images, which is independent of optical character recognition (OCR) or layout analyses. The text area is extracted after preprocessing including skew correction and marginal noise removal. Then the aspect ratio and multi-density features are extracted from the text area to select the best candidates from the document image database. Experimental results show that this approach is simple with loss rates less than 2% and can efficiently analyze images with different resolutions and different input systems. The system is also robust to noise due to such as notes and complex layouts.