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A Survey on the Classifiers in On-line Handwritten Uyghur Character Recognition System
Wujiahemaiti Simayi,Mayire Ibrayim,Dilmurat Tursun,Askar Hamdulla 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.3
With the fast development of information technology made people eager to get access the convenient implementations of modern technology in every walk of life. Online handwritten recognition technology for Uyghur is also receiving great need, too. Precious work form researchers for this technology has been gifted many gains. This paper observe the classifiers used in previous work on this field in order to see their adaptabilities for Uyghur online handwritten recognition, and acquire clues for classifier implementation in future work.
Survey on the Features for Recognition of on-line Handwritten Uyghur Characters
Wujiahemaiti Simayi,Mayire Ibrayim,Dilmurat Tursun,Askar Hamdulla 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.9
This survey attempts to provide a detailed overview of the previous work on Uyghur on-line handwritten character recognition technology and mainly focuses on investigating the feature extraction algorithms that have been presented or applied to Uyghur characters. Statistical, structural and combined-hybrid approaches for feature extraction technology for Uyghur characters are going to be explored. Besides the popular feature extraction methods which perhaps are granted to be taken into consideration for all letter kinds, the study on Uyghur handwritten recognition contributed new features and extraction algorithms for pattern recognition. Summarization on observing methods and extracted features from the published work and suggestions for further attempts would be valuable reference for the up-coming research activities on finding more efficient features.
Askar Hamdulla,Wujiahemaiti Simayi,Mayire Ibrayim,Dilmurat Tursun 보안공학연구지원센터 2014 International Journal of Signal Processing, Image Vol.7 No.5
Through the analysis on the unique characteristics of Uyghur characters, in order to further improve the recognition rate, this paper developed the Center Distance Feature (CDF) to its modified form which is named as Modified Center Distance Feature (MCDF). By combination with some low dimensional features including stroke number feature, additional part’s location feature, shape feature, bottom-up and left-right density feature(BULR) in experiments, MCDF gifted robust recognition accuracy of 98.77% for the 32 isolated forms of Uyghur characters. MCDF increased the recognition accuracy by 4.51 points comparing with the result from the combination of CDF with the same low dimensional features mentioned above, which is 94.16%. This paper used the samples from 400 different volunteers. The recognition system is trained using 70 percent of 12800 samples from 400 different writers and tested on the remained 30 percent.