Robust Unsupervised Segmentation of Degraded Document Images with Topic Models

Segmentation of document images remains a challenging vision problem. Although document images have a structured layout, capturing enough of it for segmentation can be difficult. Most current methods combine text extraction and heuristics for segmentation, but text extraction is prone to failure and measuring accuracy remains a difficult challenge. Furthermore, when presented with significant degradation many common heuristic methods fall apart. In this paper, we propose a Bayesian generative model for document images which seeks to overcome some of these drawbacks. Our model automatically discovers different regions present in a document image in a completely unsupervised fashion. We attempt no text extraction, but rather use discrete patch-based codebook learning to make our probabilistic representation feasible. Each latent region topic is a distribution over these patch indices. We capture rough document layout with an M R F Potts model. We take an analysis-by-synthesis approach to examine the model, and provide quantitative segmentation results on a manually-labeled document image data set. We illustrate our model's robustness by providing results on a highly degraded version of our test set.