Edge detection algorithms are important tools in image processing applications for carrying out much information and being relatively easy to produce. Sobel; Canny; and logarithmic algorithms [1] are among several edge detection algorithms used frequently nowadays. The evalution of such edge detection algorithms is an old problem. Authors [1][3] tend to use visual evaluation that limits the comparison between different edge images. In this paper, we present a new edge enhancement method and five different measures that can be used to statistically evaluate edge detection algorithms. The new edge enhancement method is based on cooperation between different edge detection algorithms. The new edge preserves the advantages of each edge image. Experimental results using two edge detection algorithms proved the efficiency of this method.
Abstract:In this paper a new binarization algorithm for ancient manuscripts and historical documents with bleeding noise has been proposed. This algorithm consists of three primary processes. In the first process, a given gray-scale image has been classified into three classes: black-foreground pixels class, white-background pixels class and confused pixels class. In the second process, the confused pixels class will be classified into either of the two black and white classes. The classified image was cut into rectangles using the confused-pixels vertical and horizontal histograms. Each rectangle is a sub-image containing a region of the image with pixels having similar properties. The third is a voting process where a threshold value is selected to binarize each sub-image separately. Seven thresholding values driven from six different global binarization techniques contribute to the voting process. The binarized image is the collection of the sub-images binarization results. Four different measuring metrics have been used to evaluate the results of the proposed algorithm. The performance of the algorithm has been compared with two widely used binarization algorithms which yield a significant improvement in the binarization process of ancient manuscripts and historical documents with bleeding noise.
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