This paper presents a novel signature feature representation for retrieving degraded binary document images based on graphical content that is rotation, resolution and translation insensitive. We use logos as an example of graphical regions in document images. Logos are arbitrarily complex in geometry and tend to be highly degraded. The first stage of signature extraction normalizes the logo with respect to geometrical variations using principal component analysis. The second stage extracts the wavelet projection signature representation which consists of the low-pass wavelet transform coefficients of the projections of the normalized image. Images are retrieved based on L1 distance in the wavelet projection signature space from the query. We present an exhaustive performance evaluation of retrieval performance on a database of over 2000 real-world degraded logo images. The retrieval performance as quantified by the percentage of queries where the target is in the top 16 logos retrieved from the database (in terms of distance from the query) ranges between 88 and 95%. We also synthetically degrade the logo images to study retrieval performance as a function of rotation, resolution and pixel noise introduced using the Baird document defect model and present the results of these evaluations in the paper.
Mysore Y. Jaisimha,
"Wavelet features for similarity-based retrieval of logo images", Proc. SPIE 2660, Document Recognition III, (7 March 1996); doi: 10.1117/12.234694; https://doi.org/10.1117/12.234694