Maximization of mutual information for offline Thai handwriting recognition
Maximization of mutual information for offline Thai handwriting recognition
复制标题
DOI：
10.1109/tpami.2006.167
复制
发表时间：
2006-08
影响因子：
23.6
通讯作者：
Roongroj Nopsuwanchai;A. Biem;W. Clocksin
中科院分区：
文献类型：
--
作者：
Roongroj Nopsuwanchai;A. Biem;W. Clocksin
本文旨在提高性能的HMM为基础的离线泰国手写识别系统，通过区分训练和微调的特征提取方法的使用。通过最大化数据与其类别之间的互信息来实现区分性训练。特征提取是基于我们提出的基于块的PCA和合成图像，证明是更好地区分泰国易混淆的字符。我们证明了显着改善识别精度相比，分类器，没有歧视性优化
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