Address block localization for Chinese postal envelopes with clutter background

In this paper we propose a novel supervised model to localize the address block for Chinese postal envelopes. The problem is formulated as a binary classification problem. We get the probability map via joint Conditional Random Field (CRF) training and dictionary learning. Histograms of Oriented Gradients (HOG) are used as descriptors. We evaluate our model on a challenging Chinese postal envelope database with clutter background. Experiment results demonstrate our model performs well and is robust to appearance variations in illumination, rotation, and clutter background.