Document Segmentation Using Pixel-Accurate Ground Truth

We compare methodologies for trainable document image content extraction, using a variety of ground-truth policies: loose, tight, and pixel-accurate. The goal is to achieve pixel-accurate segmentation of document images. Which ground-truth policy is the best has been debated. ``Loose'' truth is obtained by sweeping rectangles to enclose entire text blocks etc, and can be an efficient manual task. ``Tight'' truth requires more care, and more time, to enclose individual text lines. Pixel-accurate truth, in which only foreground pixels are labeled, can be obtained by applying the PARC PixLabeler tool; in our experience this tool was as quick to use as loose truthing. We have compared the accuracy of all three truthing policies, and report that tight truth supports higher accuracy than loose truth, and pixel-accurate truth yields the highest accuracy. We have also experimented on morphological expansions on pixel-accurate truth, by expanding sets of foreground pixels morphologically, and report that expanded pixel-accurate truth supports higher accuracy than pixel-accurate truth.