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CASIA OpenIR
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LG-CNN: From local parts to global discrimination for fine-grained recognition 期刊论文
PATTERN RECOGNITION, 2017, 卷号: 71, 期号: 71, 页码: 118-131
作者:  Xie, Guo-Sen;  Zhang, Xu-Yao;  Yang, Wenhan;  Xu, Mingliang;  Yan, Shuicheng;  Liu, Cheng-Lin
浏览  |  Adobe PDF(2717Kb)  |  收藏  |  浏览/下载：704/277  |  提交时间：2018/01/16
Fine-grained Recognition  Convolutional Neural Networks  Bilinear Pooling  Local Parts  Global Discrimination  
Scene Text Detection with Novel Superpixel Based Character Candidate Extraction 会议论文
, Kyoto, Japan, November 10-15, 2017
作者:  Wang, Cong;  Yin, Fei;  Liu, Cheng-Lin
浏览  |  Adobe PDF(856Kb)  |  收藏  |  浏览/下载：335/104  |  提交时间：2018/01/05
Scene Text Detection  Superpixel  Hierarchical Clustering  
Traffic Sign Detection Using a Cascade Method With Fast Feature Extraction and Saliency Test 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2017, 卷号: 18, 期号: 12, 页码: 3290-3302
作者:  Wang, Dongdong;  Hou, Xinwen;  Xu, Jiawei;  Yue, Shigang;  Liu, Cheng-Lin;  Cheng-Lin Liu
浏览  |  Adobe PDF(2774Kb)  |  收藏  |  浏览/下载：553/174  |  提交时间：2018/01/05
Traffic Sign Detection  Cascade System  Fast Feature Extraction  Saliency Test  
Keyword spotting in handwritten chinese documents using semi-markov conditional random fields 期刊论文
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2017, 卷号: 58, 页码: 49-61
作者:  Zhang, Heng;  Zhou, Xiang-Dong;  Liu, Cheng-Lin
Adobe PDF(1932Kb)  |  收藏  |  浏览/下载：235/12  |  提交时间：2017/05/05
Online Handwritten Chinese Documents  Semi-markov Conditional Random Fields  Keyword Spotting  Proxy-character Driven Search  
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
浏览  |  Adobe PDF(1290Kb)  |  收藏  |  浏览/下载：1210/520  |  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