NDLI: A method for assessing norms of Chinese character based on K-means clustering and hough transformation

Content Provider	IEEE Xplore Digital Library
Author	Kunyu Wang Zhiyi Qu Jianfei Sun
Copyright Year	2014
Description	Author affiliation: Sch. of Inf. & Eng., Lanzhou Univ., Lanzhou, China (Kunyu Wang; Zhiyi Qu; Jianfei Sun)
Abstract	In this paper, we process the image of Chinese characters, and assess the regular of Chinese characters in the image. Images of Chinese handwritten characters is processed to remove the irrelevant information such as background color, background noise. After processing and Hough transforming the image, we can get the information of strokes and angle through the outline of the image. After Hough transforming, the lengths and angles of the Hough Lines can be obtained and they are the approximate equivalent to the length and angle of strokes of Chinese characters themselves. Further analyzing the length and angles of the Hough Lines, the structure of Hough Lines can be equivalent to the structure of Chinese character strokes. Using the K-means algorithm of Clustering analysis, we grouped the degree of angles into different clusters. Because of Chinese characters strokes have vertical and horizontal features, we just need analyze the clusters near 0°, 90°, 180°, 45°, 135°. Finally,we use the length of the Hough lines as weights to assess norms of the Chinese characters.
Starting Page	1256
Ending Page	1259
File Size	1683835
Page Count	4
File Format	PDF
e-ISBN	9781479969890
DOI	10.1109/WARTIA.2014.6976510
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-29
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Handwriting recognition Image edge detection Conferences K-Means Clustering Image processing Industry applications Clustering algorithms Hough transformation Character recognition Noise measurement Assessment of the norm of Chinese characters
Content Type	Text
Resource Type	Article

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