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A modified K3M thinning algorithm

Tabedzki M., Saeed K., Szczepański A.

International Journal of Applied Mathematics and Computer Science

2016

Vol. 26, no. 2

439--450

The K3M thinning algorithm is a general method for image data reduction by skeletonization. It had proved its feasibility in most cases as a reliable and robust solution in typical applications of thinning, particularly in preprocessing for optical character recognition. However, the algorithm had still some weak points. Since then K3M has been revised, addressing the best known drawbacks. This paper presents a modified version of the algorithm. A comparison is made with the original one and two other thinning approaches. The proposed modification, among other things, solves the main drawback of K3M, namely, the results of thinning an image after rotation with various angles.

A vectorized thinning algorithm for handwritten symbols recognition

Stąpor K.

Machine Graphics and Vision

1999

Vol. 8, No. 3

341-352

In this paper a non-pixelwise thinning algorithm of binary line images, called vectorized thinning is proposed. The praesented algorithm produces a skeleton of a 2D-object which is performed in three steps: 1) links finding and simple region extraction, 2) complex region extraction and multiple points finding, 3) transformation into vectorized skeleton. As opposed to other thinning algorithms, the obtained skeleton is yet in vector from, particularly suitable for feature extraction in the cartographic symbol recognition from scanned, geodesic maps, with much better results than using other, pixelwise thinning methods ([17]). The main advantages of the proposed thinning algorithm lie in better extraction of multiple points representing corners, branch or crossing regions of 2D objects, and less sensitivity to a boundary noise which is one of the main problems in pixelwise thinning algorithms.