Iterated Document Content Classification

12 years 3 months ago
Iterated Document Content Classification
We report an improved methodology for training classifiers for document image content extraction, that is, the location and segmentation of regions containing handwriting, machine-printed text, photographs, blank space, etc. Our previous methods classified each individual pixel separately (rather than regions): this avoids the arbitrariness and restrictiveness that result from constraining region shapes (to, e.g., rectangles). However, this policy also allows content classes to vary frequently within small regions, often yielding areas where several content classes are mixed together. This does not reflect the way that real content is organized: typically almost all small local regions are of uniform class. This observation suggested a post-classification methodology which enforces local uniformity without imposing a restricted class of region shapes. We choose features extracted from small local regions (e.g. 4-5 pixels radius) with which we train classifiers that operate on the outp...
Chang An, Henry S. Baird, Pingping Xiu
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2007
Authors Chang An, Henry S. Baird, Pingping Xiu
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