Applying image descriptors to the assessment of legibility in Chinese characters.
The current study derived seven descriptors from the pixel matrix of 5401 Chinese characters in the commonly used Ming, Kai, Li, and Hei fonts. A factor analysis was used to reduce the number of descriptors for a validation experiment. The three-factor solution (number of strokes, character height, and character width) explaining 89.54% of the variance was derived from 5401 characters in four fonts. A 4 x 2 x 2 x 2 factorial experiment using fonts, number of strokes, character height, and character width as the independent variables was conducted to evaluate the impact of each critical factor on the legibility of Chinese characters. Each tested character was chosen from the 500 most commonly used characters to minimize the effect of familiarity and ensure that all participants could recognize the character if it was legible. The 16 university students who participated in the experiment were asked to identify a Chinese character initially displayed on a PC screen at its minimum size and enlarged gradually until the participant could recognize it. The analysis of variance suggests that all the main effects of font, number of strokes, character height, and character width are significant. The legibility thresholds of the four type styles from the most to the least legible are in the Hei, Ming, Kai, and Li sequence. Number of strokes is the only significant factor in predicting the legibility threshold for each individual font. However, if predicting the legibility threshold across four fonts, character height is the other significant factor with about the same predictive power as number of strokes. The legible threshold is increased by the number of strokes and decreased by the character height and character width.
Department of Industrial Management, National Taiwan University of Science and Technology, Taipei. chris@mail.ntust.edu.tw
Chia-Fen
CF
Chi
Dengchuan
D
Cai
Manlai
M
You
D000328:Adult; D002681:China; D016288:Computer Terminals; D006804:Ergonomics / Q000379:methods; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008297:Male; D011932:Reading; D012044:Regression Analysis; D014796:Visual Perception; D014956:Writing