Pham, Tuan D. Unconstrained logo detection in document images. (English) Zbl 1059.68614 Pattern Recognition 36, No. 12, 3023-3025 (2003). Summary: A fast and effective algorithm is developed for detecting logos in grayscale document images. The computational schemes involve segmentation, and the calculation of the spatial density of the defined foreground pixels. The detection does not require training and is unconstrained in the sense that the presence of a logo in a document image can be detected under scaling, rotation, translation, and noise. Several tests on different electronic document forms such as letters, faxes, and billing statements are carried out to illustrate the performance of the method. MSC: 68T10 Pattern recognition, speech recognition Keywords:Logo detection; Document imaging; Segmentation; Mountain function Software:C4.5 PDF BibTeX XML Cite \textit{T. D. Pham}, Pattern Recognition 36, No. 12, 3023--3025 (2003; Zbl 1059.68614) Full Text: DOI References: [2] Quinlan, J. R., C4.5: Programs for Machine Learning (1992), Morgan Kaufmann: Morgan Kaufmann San Mateo, CA [3] Otsu, N., A threshold selection method from gray-level histograms, IEEE Trans. Systems Man Cybernet., 9, 1, 62-66 (1979) [4] Yager, R. R.; Filev, D. P., Approximate clustering via the mountain method, IEEE Trans. Systems Man Cybernet., 24, 1279-1284 (1994) This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.