CASIA OpenIR

浏览/检索结果: 共11条,第1-10条 帮助

限定条件    
已选(0)清除 条数/页:   排序方式:
DetectGAN: GAN-based text detector for camera-captured document images 期刊论文
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION, 2020, 卷号: 23, 期号: 4, 页码: 267-277
作者:  Zhao, Jinyuan;  Wang, Yanna;  Xiao, Baihua;  Shi, Cunzhao;  Jia, Fuxi;  Wang, Chunheng
Adobe PDF(3817Kb)  |  收藏  |  浏览/下载:278/52  |  提交时间:2020/09/21
Text detection  Camera-captured document images  Multi-scale context features  Generative adversarial networks  
A benchmark for unconstrained online handwritten Uyghur word recognition 期刊论文
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION, 2020, 页码: 14
作者:  Simayi, Wujiahemaiti;  Ibrahim, Mayire;  Zhang, Xu-Yao;  Liu, Cheng-Lin;  Hamdulla, Askar
收藏  |  浏览/下载:129/0  |  提交时间:2020/08/31
Online handwriting recognition  Uyghur alphabet  Database  Out-of-vocabulary words  Recurrent neural network  1D Convolution  
Scene text detection and recognition with advances in deep learning: a survey 期刊论文
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION, 2019, 卷号: 22, 期号: 2, 页码: 143-162
作者:  Liu, Xiyan;  Meng, Gaofeng;  Pan, Chunhong
Adobe PDF(2418Kb)  |  收藏  |  浏览/下载:269/30  |  提交时间:2019/07/11
Natural image  Text detection  Text recognition  Survey  
Partial discriminative training for classification of overlapping classes in document analysis 期刊论文
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION, 2008, 卷号: 11, 期号: 2, 页码: 53-65
作者:  Liu, Cheng-Lin
收藏  |  浏览/下载:184/0  |  提交时间:2015/11/08
Character Recognition  Overlapping Classes  Discriminative Training  Partial Discriminative Training  
A character image restoration method for unconstrained handwritten Chinese character recognition 期刊论文
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION, 2015, 卷号: 18, 期号: 1, 页码: 73-86
作者:  Shao, Yunxue;  Wang, Chunheng;  Xiao, Baihua
浏览  |  Adobe PDF(1157Kb)  |  收藏  |  浏览/下载:299/64  |  提交时间:2015/09/21
Character Image Restoration  Unconstrained Handwritten Chinese Character Recognition  Similar Character Discrimination  
Fast self-generation voting for handwritten Chinese character recognition 期刊论文
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION, 2013, 卷号: 16, 期号: 4, 页码: 413-424
作者:  Shao, Yunxue;  Wang, Chunheng;  Xiao, Baihua
收藏  |  浏览/下载:209/0  |  提交时间:2015/08/12
Handwritten Chinese Character Recognition  Fast Self-generation Voting  Line Density Equalization  Normalization-cooperated Feature Extraction  Modified Quadratic Discriminant Function  
Visual word density-based nonlinear shape normalization method for handwritten Chinese character recognition 期刊论文
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION, 2013, 卷号: 16, 期号: 4, 页码: 387-397
作者:  Shao, Yunxue;  Wang, Chunheng;  Xiao, Baihua
收藏  |  浏览/下载:197/0  |  提交时间:2015/08/12
Visual Word Density  Handwritten Chinese Character Recognition  Character Shape Normalization  Scene Character Recognition  
An evaluation of statistical methods in handwritten hangul recognition 期刊论文
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION, 2013, 卷号: 16, 期号: 3, 页码: 273-283
作者:  Park, Gyu-Ro;  Kim, In-Jung;  Liu, Cheng-Lin
收藏  |  浏览/下载:159/0  |  提交时间:2015/08/12
Handwritten Hangul Recognition  Statistical Methods  Character Normalization  Feature Extraction  Classification  
An over-segmentation method for single-touching Chinese handwriting with learning-based filtering 期刊论文
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION, 2014, 卷号: 17, 期号: 1, 页码: 91-104
作者:  Xu, Liang;  Yin, Fei;  Wang, Qiu-Feng;  Liu, Cheng-Lin
浏览  |  Adobe PDF(1843Kb)  |  收藏  |  浏览/下载:264/61  |  提交时间:2015/08/12
Single-touching Strings  Chinese Handwriting  Over-segmentation  Learning-based Filtering  Geometric Features  
Learning confidence transformation for handwritten Chinese text recognition 期刊论文
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION, 2014, 卷号: 17, 期号: 3, 页码: 205-219
作者:  Wang, Da-Han;  Liu, Cheng-Lin
收藏  |  浏览/下载:177/0  |  提交时间:2015/08/12
Handwritten Text Recognition  Confidence Learning  Parametric And nonParametric  Class-dependent And Class-independent  String-level Learning