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CASIA OpenIR
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Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models 期刊论文
PATTERN RECOGNITION, 2017, 卷号: 2017, 期号: 65, 页码: 251-264
作者:  Wu, Yi-Chao;  Yin, Fei;  Liu, Cheng-Lin
Adobe PDF(1290Kb)  |  收藏  |  浏览/下载：1129/498  |  提交时间：2017/02/18
Handwritten Chinese Text Recognition  Feedforward Neural Network Language Model  Recurrent Neural Network Language Model  Hybrid Language Model  Convolutional Neural Network Shape Models  
Adaptive spatial pooling for image classification 期刊论文
PATTERN RECOGNITION, 2016, 卷号: 55, 页码: 58-67
作者:  Liu, Yinglu;  Zhang, Yan-Ming;  Zhang, Xu-Yao;  Liu, Cheng-Lin
Adobe PDF(1748Kb)  |  收藏  |  浏览/下载：597/219  |  提交时间：2016/04/06
Weighted Pooling  Spatial Layout  Distribution Matrix  Image Classification  
Minimum-risk training for semi-Markov conditional random fields with application to handwritten Chinese/Japanese text recognition 期刊论文
PATTERN RECOGNITION, 2014, 卷号: 47, 期号: 5, 页码: 1904-1916
作者:  Zhou, Xiang-Dong;  Zhang, Yan-Ming;  Tian, Feng;  Wang, Hong-An;  Liu, Cheng-Lin
浏览  |  Adobe PDF(751Kb)  |  收藏  |  浏览/下载：263/68  |  提交时间：2015/08/12
Semi-markov Conditional Random Fields  Minimum-risk Training  Character String Recognition  
Unsupervised language model adaptation for handwritten Chinese text recognition 期刊论文
PATTERN RECOGNITION, 2014, 卷号: 47, 期号: 3, 页码: 1202-1216
作者:  Wang, Qiu-Feng;  Yin, Fei;  Liu, Cheng-Lin
浏览  |  Adobe PDF(3154Kb)  |  收藏  |  浏览/下载：266/52  |  提交时间：2015/08/12
Character String Recognition  Chinese Handwriting Recognition  Unsupervised Language Model Adaptation  Language Model Compression  
Transcript mapping for handwritten Chinese documents by integrating character recognition model and geometric context 期刊论文
PATTERN RECOGNITION, 2013, 卷号: 46, 期号: 10, 页码: 2807-2818
作者:  Yin, Fei;  Wang, Qiu-Feng;  Liu, Cheng-Lin
Adobe PDF(1946Kb)  |  收藏  |  浏览/下载：297/59  |  提交时间：2015/08/12
Transcript Mapping  Minimum Classification Error (Mce)  Dynamic Time Warping (Dtw)  Confidence Transformation (Ct)  Geometric Context