Global automatic thresholding with edge information and moving average on histogram
Yu-Kumg Chen, Yi‐Fan Chang · 2006
Optical character recognition occupies a very important field in digital image processing. It is used extensively in daily life. If the given image does not have a bimodal intensity histogram, it would cause segmenting mistake easily for the previous bi-level algorithms. In order to solve this problem, a new algorithm is proposed in this paper. The proposed algorithm uses the theory of moving average on the histogram of the fuzzy image, and then derives the better histogram. Since use only one thresholding value cannot solve this problem completely, the edge information and the window processing are introduced in this paper for advanced thresholding. Thus, a more refine bi-level image is derived and it will result in the improvement of optical character recognition. Experiments are carried out for some samples with shading to demonstrate the computational advantage of the proposed method.