Document image retrieval based on multi-density features

Abstract

The development of document image databases is becoming a challenge for document image retrieval techniques. Traditional layout-reconstructed-based methods rely on high quality document images as well as an optical character recognition (OCR) precision, and can only deal with several widely used languages. The complexity of document layouts greatly hinders layout analysis-based approaches. This paper describes a multi-density feature based algorithm for binary document images, which is independent of OCR or layout analyses. The text area was extracted after preprocessing such as skew correction and marginal noise removal. Then the aspect ratio and multi-density features were 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 3% and can efficiently analyze images with different resolutions and different input systems. The system is also robust to noise due to its notes and complex layouts, etc.