Minimum error rate training for PHMM-based text recognition

Author

Yen, Chinching ; Kuo, Shyh-shiaw ; Lee, Chin-Hui

Author_Institution

Lucent Technol., AT&T Bell Labs., Holmdel, NJ, USA

Volume

Issue

fYear

1999

fDate

8/1/1999 12:00:00 AM

Firstpage

1120

Lastpage

1124

Abstract

Discriminative training is studied to improve the performance of our pseudo two-dimensional (2-D) hidden Markov model (PHMM) based text recognition system. The aim of this discriminative training is to adjust model parameters to directly minimize the classification error rate. Experimental results have shown great reduction in recognition error rate even for PHMMs already well-trained using conventional maximum likelihood (ML) approaches

Keywords

classification; error statistics; hidden Markov models; image recognition; MLE; PHMM-based text recognition; classification error rate minimisation; discriminative training; experimental results; maximum likelihood estimation; minimum error rate training; model parameters adjustment; performance; pseudo 2D hidden Markov model; recognition error rate reduction; Document image processing; Error analysis; Hidden Markov models; Image recognition; Maximum likelihood estimation; Parameter estimation; Pattern classification; Pattern recognition; Text recognition; Two dimensional displays;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.777092

Filename

777092

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1532463