A New Feature Ranking Method in a HMM-Based Handwriting Recognition System

Authors:
Sijun Kang;Venu Govindaraju
Affiliations:
CEDAR, State University of New York at Buffalo;CEDAR, State University of New York at Buffalo
Venue:
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Year:
2005

Citing 3
Cited 0

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Building Skeletal Graphs for Structural Feature Extraction on Handwriting Images

ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition

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Abstract

In this paper we propose a new feature ranking method in a recognition system, by introducing the concept of the effectiveness of the distinguishing power of features and considering the correlation among features. To find the subset of most important features, first the best feature can be identified by its effective distinguishing power and put in an empty feature set. Then each of the remaining features is ranked based on their effective distinguishing capacity contribution and the highest-ranked feature is added to the selected subset. This process is repeated till the performance of the system reaches its peak or the effective distinguishing contribution falls below a certain value. The application of this method to an existing handwriting recognition system showed strong support for our methodology of feature ranking.