Abstract
Image Segmentation plays an important role in image processing and analysis. In order to preserve strokes of a Chinese character while enhancing character details for degraded historical document image, we propose an adaptive segmentation algorithm for degraded historical document image binarization based on background estimation for non-uniform illumination images. The novelty of the proposed method is that find an optimal background estimation based on Blind/Referenceless Image Spatial QUality Evaluator. The proposed method has four steps: (i) preprocess using median filtering; (ii) extraction of the red color components; (iii) a morphological operation in order to find an optimal background estimation; and (iv) segmented binary image using Otsu’s Thresholding. Experimental results demonstrate that it is capable of extracting more accurate segmentation of characters for degraded Chinese rubbing document image.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (Grant No. 61472173), Natural Science Foundation of Jiangxi Province of China, No. 20161BAB202042, the grants from the Educational Commission of Jiangxi province of China, No. GJJ151134.
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Huang, H., Huang, ZK., Ma, YL., Hou, LY. (2018). An Adaptive Segmentation Algorithm for Degraded Chinese Rubbing Image Binarization Based on Background Estimation. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_3
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