Salient Features and Hypothesis Testing: evaluating a novel approach for segmentation and address block location

Abstract

This paper presents a modification with further experiments of a segmentation algorithm based on feature selection in wavelet space of ours [9]. The aim is to automatically separate in postal envelopes the regions related to background, stamps, rubber stamps, and the address blocks. First, a typical image of a postal envelope is decomposed using Mallat algorithm and Haar basis. High frequency channel outputs are analyzed to locate salient points in order to separate the background. A statistical hypothesis test is taken to decide upon more consistent regions in order to clean out some noise left. The selected points are projected back to the original gray level image, where the evidence from the wavelet space is used to start a growing process to include the pixels more likely to belong to the regions of stamps, rubber stamps, and written area. We have modified the growing process controlled by the salient points and the results were greatly improved reaching success rate of over 97%. Experiments are run using original postal envelopes from the Brazilian Post Office Agency, and here we report results on 440 images with many different layouts and backgrounds.