Iterated Document Content Classification

Abstract

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 output of previous classifiers, guided by ground truth. This provides a sequence of post-classifiers, each trained separately on the results of the previous classifier. Experiments on a highly diverse test set of 83 document images show that this method reduces per-pixel classification errors by 23%, and it dramatically increases the occurrence of large contiguous regions of uniform class, thus providing highly usable near-solid 'masks' with which to segment the images into distinct classes. It continues to allow a wide range of complex, non-rectilinear region shapes.