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, we propose a unified approach for document segmentation. Differently of others techniques that segment images without <i>a priori</i> knowledge about the classes to be segmented, this approach carries out a previous learning of what must be segmented. The learning is carried out using only two images, the original one and its ideal segmented version. This stage generates a decision matrix, which is used to extract the similar semantic information in new images. The knowledge acquired in the decision matrix is explored by means of KNN strategy. Performed tests on different types of document images, like signature, postal envelopes and old document databases for instance, showed significant and promising results. It must be emphasized that this learning segmentation approach is completely automatic, does not require heuristics, and may transform the subjective human operator's knowledge into an automatic process and reproduce it.
This article presents a new segmentation method of complex postal envelopes by mixture approach combining the concepts of mathematical morphology and of co-occurrence matrix. Morphological segmentation techniques will assist to interpret the information generated by the co-occurrence matrix for extracting the contents of the brazilian postal envelopes. Very little <i>a priori </i>knowledge of the envelope images is required. The advantages of this approach will be described and illustrated with tests carried out on 250 different complex envelope images where there is no fixed position for the handwritten address block, postmarks and stamps.
Proc. SPIE. 3572, 3rd Iberoamerican Optics Meeting and 6th Latin American Meeting on Optics, Lasers, and Their Applications
KEYWORDS: Image processing algorithms and systems, Lithium, Safety, Detection and tracking algorithms, Image segmentation, Image processing, Scanners, Image quality, Optical character recognition, RGB color model
This article describes an approach to quantify the quality of thresholding-based segmentation on Brazilian bankchecks. The checks are previously acquired by scanner and soon after, their colored patterns are decomposed according to the HSI model. Several thresholding algorithms are then applied and an automatic evaluation of the results is carried out. The evaluation methodology is based on `performance assessment' of the character recognition rate from an optical character recognition.