Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/TPAMI.2006.167 | Pattern Analysis and Machine Intelligence, IEEE Transactions |
Keywords | Field | DocType |
feature extraction,handwritten character recognition,hidden Markov models,learning (artificial intelligence),optimisation,principal component analysis,HMM,Thai confusable characters,block-based PCA,composite images,discriminative training,fine-tuned feature extraction methods,hidden Markov models,mutual information maximization,offline Thai handwriting recognition,principal component analysis,Character recognition,Hidden Markov Model,PCA,Thai handwriting recognition.,discriminative training,feature extraction | Pattern recognition,Computer science,Image processing,Handwriting recognition,Speech recognition,Feature extraction,Mutual information,Artificial intelligence,Hidden Markov model,Discriminative model,Maximization,Principal component analysis | Journal |
Volume | Issue | ISSN |
28 | 8 | 0162-8828 |
Citations | PageRank | References |
11 | 0.76 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roongroj Nopsuwanchai | 1 | 11 | 0.76 |
| Alain Biem | 2 | 288 | 18.64 |
| William F Clocksin | 3 | 11 | 0.76 |