DocumentCode
2650182
Title
Does There Exist Relationship between Personality and Handwriting of Chinese Characters? A View from Image Mining
Author
Chen, Zhanghui ; Zhou, Baoyao ; Fong, Alvis C M
Author_Institution
Inst. of Semicond., Beijing, China
fYear
2011
fDate
7-9 Nov. 2011
Firstpage
229
Lastpage
232
Abstract
This paper presents a study on the relationship between personality and handwriting of Chinese characters through image mining technologies. A questionnaire of personality test is used to quantify the 5 global personality factors of participants. The handwriting samples of participants are acquired and scanned into computer images. 23 handwriting features can be extracted from these sample images through image processing methods. Considering the imbalanced distribution of the sample data, a cost-sensitive neural network with modified training algorithms and correlation analysis are employed to examine the association between the handwriting features and global personality factors. The results hint that there indeed exist some weak linear and strong non-linear relationships between most of personality factors and specific handwriting features. These relationships provide the possibility for computerized analyzing people´s personality by their Chinese handwriting.
Keywords
data mining; feature extraction; handwriting recognition; handwritten character recognition; image scanners; neural nets; Chinese character handwriting; computer image scanning; correlation analysis; cost-sensitive neural network; handwriting feature extraction; image mining; image processing method; modified training algorithm; people personality computerized analysis; personality test; Artificial intelligence; Conferences; correlation analysis; cost-sensitive neural network; handwriting analysis; image mining;
fLanguage
English
Publisher
ieee
Conference_Titel
Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
Conference_Location
Boca Raton, FL
ISSN
1082-3409
Print_ISBN
978-1-4577-2068-0
Electronic_ISBN
1082-3409
Type
conf
DOI
10.1109/ICTAI.2011.42
Filename
6103332
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