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In this paper, we propose an approach to automatically locate address blocks in postal envelopes based on fractal dimension. First, the fractal dimension of a postal envelope image is computed. The K-means clustering technique is then used to label pixels as stamps, postmarks, and address blocks.

In this paper, an approach based on lacunarity to locate address blocks in postal envelopes is proposed. After computing the lacunarity of a postal envelope image, a non-linear transformation is applied on it. A thresholding technique is then used to generate evidences. Finally, a region growing is applied to reconstruct semantic objects like stamps, postmarks, and address blocks. Very little a priori knowledge of the envelope images is required. By using the lacunarity for several ranges of neighbor window sizes r onto 200 postal envelope images, the proposed approach reached a success rate over than 97% on average.

Abstract. In this paper, an address block segmentation approach based on fractal dimension F D is proposed. After computing the fractal dimension of each image pixel by the 2D variation procedure, a clustering technique based on K-means is used to label pixels into semantic objects. The evaluation of the efficiency is carried out from a total of 200 postal envelope images with no fixed position for the address block, postmark and stamp. A ground-truth strategy is used to achieve an objective comparison. Experiments showed significant and promising results. By using the 2D variation procedure for three ranges of neighbor window sizes (r = {3, 5}, r = {3, 5, 7}, and r = {3, 5, 7, 9}), the proposed approach reached a success rate over than 90% on average.

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This paper introduces a novel approach for region segmentation. In order to represent the regions, we devise and test new features based on low and high frequency wavelet coefficients which allow to capture and judge regions using changes in brightness and texture. A fusion process through statistical hypothesis testing among regions is established in order to obtain the final segmentation. The proposed local features are extracted from image data driven by global statistical information. Preliminary experiments show that the approach can segment both texturized and regions cluttered with edges, demonstrating promising results. Hypothesis testing is shown to be effective in grouping even small patches in the process.