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Additional resources for 43.Pattern Analysis and Machine Intelligence
Text/Graphics Separation. Separating machine-printed text from handwritten annotations is necessary for invoking appropriate recognition algorithms. One method that performs this discrimination well with postal addresses is based on computing the histogram of heights of the connected components. Handwritten components tend to have a wider distribution in heights than print. R32: R43: Headlines occupying more than one printed line are left-justified Captions are always below photographs, unless two or more photographs have a common caption Explicit boxes around blocks signify an independent unit.
M. ), Readings in Nonmonotonic Reasoning. Los Altos, California: Morgan Kaufmann Publishers, 1987. 23. G. R. Martinez, An integrated framework for learning and reasoning. Journal of Artiﬁcial Intelligence Research, 3: 147–185, 1995. 24. J. Glasgow and D. Papadias, Computational imagery. Cognitive Science, 16: 355–394, 1992. 25. F. Gobet, Roles of pattern recognition and search in expert problem solving. Thinking and Reasoning, 3: 291–313, 1997. 26. A. Groeger, Memory and Remembering. Hong Kong: Longman, 1997.
In low-quality or nonuniform text images these sophisticated algorithms may not correctly extract characters and thus, recognition errors may occur. Recognition of unconstrained handwritten text can be very difficult because characters cannot be reliably isolated especially when the text is cursive handwriting. Document Understanding Document understanding (DU) is the goal-oriented task of deriving a symbolic representation of the contents of a document image, which involves detecting and interpreting different blocks (like photographs, text, and drawings), accounting for the interactions of the different components, and coordinating the interpretations to achieve an end result.