Achieving high recognition reliability using decision trees and AdaBoost

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationDocument Recognition and Retrieval XV
DOIs
StatePublished - 2008
EventDocument Recognition and Retrieval XV - San Jose, CA, United States
Duration: 29 Jan 200831 Jan 2008

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6815
ISSN (Print)0277-786X

Conference

ConferenceDocument Recognition and Retrieval XV
Country/TerritoryUnited States
CitySan Jose, CA
Period29/01/0831/01/08

Keywords

  • Bangla numeral recognition
  • Decision tree
  • Recognition reliability
  • WRB AdaBoost

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