Abstract
Understanding printed documents such as newspapers is a common intelligent activity of humans. Making a computer perform the task of analyzing a newspaper image and derive useful high-level representations requires the development and integration of techniques in several areas, including pattern recognition, computer vision, language understanding and artificial intelligence. We describe the organization and several components of a newspaper image undertanding system that begins with digitized images of newspaper pages and produces symbolic representations at several different levels. Such representations include: the visual sketch (connected components extracted from the background), physical layout (spatial extents of blocks corresponding to text, half-tones, graphics), logical layout (organization of story components), block primitives (e.g., recognized characters and words in text blocks, lines in graphics, faces in photographs, etc.), and semantic nets corresponding to photographic and textual blocks (individually, as well as grouped together as stories). We describe algorithms for deriving several of the representations and describe the interaction of different modules.
This work was supported by the National Science Foundation grant IRI-86-13361 and by a grant from the Eastman Kodak Company.
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© 1990 Springer-Verlag Berlin Heidelberg
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Govindaraju, V. et al. (1990). Newspaper image understanding. In: Ramani, S., Chandrasekar, R., Anjaneyulu, K.S.R. (eds) Knowledge Based Computer Systems. KBCS 1989. Lecture Notes in Computer Science, vol 444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0018395
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DOI: https://doi.org/10.1007/BFb0018395
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