Quantitative performance evaluation of thinning algorithms under noisy conditions

Thinning algorithms are an important sub-component in the construction of computer vision (especially for optical character recognition (OCR)) systems. Important criteria for the choice of a thinning algorithm include the sensitivity of the algorithms to input shape complexity and to the amount of noise. In previous work, we introduced a methodology to quantitatively analyse the performance of thinning algorithms. The methodology uses an ideal world model for thinning based on the concept of Blum ribbons. In this paper we extend upon this methodology to answer these and other experimental questions of interest. We contaminate the noise-free images using a noise model that simulates the degradation introduced by the process of xerographic copying and laser printing. We then design experiments that study how each of 16 popular thinning algorithms performs relative to the Blum ribbon gold standard and relative to itself as the amount of noise varies. We design statistical data analysis procedures for various performance comparisons. We present the results obtained from these comparisons and a discussion of their implications in this paper