Achieving high recognition reliability using decision trees and AdaBoost

Jianying Xiang; Xiao Tu; Yue Lu; Patrick S. P. Wang

doi:10.1117/12.766059

28 January 2008 Achieving high recognition reliability using decision trees and AdaBoost

Jianying Xiang, Xiao Tu, Yue Lu, Patrick S. P. Wang

Author Affiliations +

Proceedings Volume 6815, Document Recognition and Retrieval XV; 68150V (2008) https://doi.org/10.1117/12.766059
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

Recognition rate is traditionally used as the main criterion for evaluating the performance of a recognition system. High recognition reliability with low misclassification rate is also a must for many applications. To handle the variability of the writing style of different individuals, this paper employs decision trees and WRB AdaBoost to design a classifier with high recognition reliability for recognizing Bangla handwritten numerals. Experiments on the numeral images obtained from real Bangladesh envelopes show that the proposed recognition method is capable of achieving high recognition reliability with acceptable recognition rate.

Citation Download Citation

Jianying Xiang, Xiao Tu, Yue Lu, and Patrick S. P. Wang "Achieving high recognition reliability using decision trees and AdaBoost", Proc. SPIE 6815, Document Recognition and Retrieval XV, 68150V (28 January 2008); https://doi.org/10.1117/12.766059

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE