Please use this identifier to cite or link to this item: https://doi.org/10.1007/11669487_24
Title: Finding the best-fit bounding-boxes
Authors: Yuan, B. 
Kwoh, L.K. 
Tan, C.L. 
Issue Date: 2006
Source: Yuan, B.,Kwoh, L.K.,Tan, C.L. (2006). Finding the best-fit bounding-boxes. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3872 LNCS : 268-279. ScholarBank@NUS Repository. https://doi.org/10.1007/11669487_24
Abstract: The bounding-box of a geometric shape in 2D is the rectangle with the smallest area in a given orientation (usually upright) that complete contains the shape. The best-fit bounding-box is the smallest bounding-box among all the possible orientations for the same shape. In the context of document image analysis, the shapes can be characters (individual components) or paragraphs (component groups). This paper presents a search algorithm for the best-fit bounding-boxes of the textual component groups, whose shape are customarily rectangular in almost all languages. One of the applications of the best-fit bounding-boxes is the skew estimation from the text blocks in document images. This approach is capable of multi-skew estimation and location, as well as being able to process documents with sparse text regions. The University of Washington English Document Image Database (UW-I) is used to verify the skew estimation method directly and the proposed best-fit bounding-boxes algorithm indirectly. © Springer-Verlag Berlin Heidelberg 2005.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/43276
ISBN: 3540321403
ISSN: 03029743
DOI: 10.1007/11669487_24
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