Clustering and Segmentation with Application in Document Image Processing
In this dissertation, we introduce a set of algorithms for document image process- ing, which are in the research area of color clustering and binarization. Color quantization algorithms are used to select a small number of colors that can accurately represent the content of a particular image. In this research, we introduce a novel color quantization algorithm which is based on the minimization of a modified Lp norm rather than the more traditional L2 norm associated with mean square error (MSE) . We demonstrate that the Lp optimization approach has two advantages. First, it produces more accurate perceived quality results, especially for important colors in small regions; and second, the norm’s value can be used as an effective criterion for selecting the minimum number of colors necessary to achieve accurate representation of the image. Binarization algorithms are used to create a binary representation of a raster document image, typically with the intent of identifying text and separating it from background content. In this work, we propose a binarization algorithm via one-pass local classification . The algorithm first generates the initial binarization results by local thresholding, then corrects the results using a one-pass local classification strategy, followed by the process of component inversion. The experimental results demonstrate that our algorithm achieves a much lower binarization error rate than other popular binarization/thresholding algorithms. It is also demonstrated that the proposed algorithm achieves a somewhat lower binarization error rate than the state-of-the-art algorithm COS , while requiring significantly less computation.
Bouman, Purdue University.
Electrical engineering|Computer science
Off-Campus Purdue Users:
To access this dissertation, please log in to our