Maximization of Mutual Information for Offline Thai Handwriting Recognition

Authors:
Roongroj Nopsuwanchai;Alain Biem;William F. Clocksin
Affiliations:
-;-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2006

Citing 3
Cited 3

The Development of the Feature Extraction Algorithms for Thai Handwritten Character Recognition System

IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Off-Line Character Recognition Using HMM by Multiple Directional Feature Extraction and Voting with Bagging Algorithm

ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
A System for Recognizing Vietnamese Document Images Based on HMM and Linguistics

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

Binary segmentation with neural validation for cursive handwriting recognition

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Discriminative structure selection method of Gaussian Mixture Models with its application to handwritten digit recognition

Neurocomputing
Binary segmentation algorithm for English cursive handwriting recognition

Pattern Recognition

Quantified Score

Hi-index	0.14

Visualization

Abstract

This paper aims to improve the performance of an HMM-based offline Thai handwriting recognition system through discriminative training and the use of fine-tuned feature extraction methods. The discriminative training is implemented by maximizing the mutual information between the data and their classes. The feature extraction is based on our proposed block-based PCA and composite images, shown to be better at discriminating Thai confusable characters. We demonstrate significant improvements in recognition accuracies compared to the classifiers that are not discriminatively optimized.