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...

• Type: Conference
• Year: 2007
• Where: ICDAR
• Authors: Chang An, Henry S. Baird, Pingping Xiu
• Keywords: Document Analysis, Document Images, ICDAR 2007, Region Shapes, Small Local Regions