OCR分为文字检测和文字识别两个部分。

计算机视觉，OCR相关的顶会、重要论文。
2020-12-11 整理，共283篇论文。

2020年48篇
2019年45篇
2018年55篇
2017年35篇
2016年19篇

文章列表

@article{Chen2004,
author = {Chen, Datong and Odobez, Jean-marc},
doi = {10.1016/j.patcog.2003.06.001},
keywords = {mrf,svm,text localization,text recognition,text segmentation,video ocr},
pages = {595--608},
title = {{Text detection and recognition in images and video frames}},
volume = {37},
year = {2004}
@article{Low2004,
author = {Low, David G},
journal = {International Journal of Computer Vision},
pages = {91--110},
title = {{Distinctive image features from scale-invariant keypoints}},
url = {https://www.cs.ubc.ca/{~}lowe/papers/ijcv04.pdf},
year = {2004}
@article{Hintze2004,
author = {Hintze, John M. and Christ, Theodore J.},
doi = {10.1080/02796015.2004.12086243},
issn = {02796015},
journal = {School Psychology Review},
number = {2},
pages = {204--217},
title = {{An examination of variability as a function of passage variance in CBM progress monitoring}},
volume = {33},
year = {2004}
@article{Ye2005,
author = {Ye, Qixiang and Huang, Qingming and Gao, Wen and Zhao, Debin},
doi = {10.1016/j.imavis.2005.01.004},
keywords = {feature combination,multiscale wavelet feature,svm classification,text detection},
pages = {565--576},
title = {{Fast and robust text detection in images and video frames}},
volume = {23},
year = {2005}
@article{Sangeetha2006,
author = {Sangeetha, V. and Prasad, K. J.Rajendra},
doi = {10.1002/chin.200650130},
issn = {03764699},
journal = {Indian Journal of Chemistry - Section B Organic and Medicinal Chemistry},
keywords = {1-hydroxycarbazole-2-carbaldehydes,2-acetylfuro carbazoles,Benzo carbazoles,Phenyl oxopyranocarbazoles,o-aminothiophenol},
number = {8},
pages = {1951--1954},
title = {{Syntheses of novel derivatives of 2-acetylfuro[2,3-a]carbazoles, benzo[1,2-b]-1,4-thiazepino[2,3-a]carbazoles and 1-acetyloxycarbazole-2- carbaldehydes}},
volume = {45},
year = {2006}
@article{Jan2007,
archivePrefix = {arXiv},
arxivId = {arXiv:1904.09405v2},
author = {Jan, C V and Wang, Qingqing and Huang, Ye and Jia, Wenjing and He, Xiangjian and Blumenstein, Michael and Lyu, Shujing and Lu, Yue},
eprint = {arXiv:1904.09405v2},
pages = {1--14},
title = {{FACLSTM : ConvLSTM with Focused Attention for Scene Text Recognition}},
year = {2007}
@article{Hess2007,
author = {Hess, Robin and Fern, Alan},
doi = {10.1109/CVPR.2007.382989},
isbn = {1424411807},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
title = {{Improved video registration using non-distinctive local image features}},
year = {2007}
@article{Frinken2009,
author = {Frinken, Volkmar and Fischer, Andreas and Manmatha, R and Bunke, Horst and Mathematics, Applied},
keywords = {classical,done using a modification,in conjunction with a,of the ctc token,outperforms not only a,passing algorithm,recurrent neural network,that the proposed systems,the keyword spotting is,to appear in the,training set,we demonstrate},
pages = {1--14},
title = {{on Recurrent Neural Networks}},
year = {2009}
@article{Enslen2009,
author = {Enslen, Eric and Hill, Emily and Pollock, Lori and Vijay-Shanker, K.},
doi = {10.1109/MSR.2009.5069482},
isbn = {9781424434930},
journal = {Proceedings of the 2009 6th IEEE International Working Conference on Mining Software Repositories, MSR 2009},
keywords = {[Electronic Manuscript]},
pages = {71--80},
title = {{Mining source code to automatically split identifiers for software analysis}},
year = {2009}
@article{Wang2011,
author = {Wang, Kai and Babenko, Boris and Belongie, Serge},
doi = {10.1109/ICCV.2011.6126402},
isbn = {9781457711015},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
number = {4},
pages = {1457--1464},
title = {{End-to-end scene text recognition}},
year = {2011}
@article{Coates2011,
author = {Coates, Adam and Carpenter, Blake and Case, Carl and Satheesh, Sanjeev and Suresh, Bipin and Wang, Tao and Wu, David J. and Ng, Andrew Y.},
doi = {10.1109/ICDAR.2011.95},
isbn = {9780769545202},
issn = {15205363},
journal = {Proceedings of the International Conference on Document Analysis and Recognition, ICDAR},
keywords = {Robust reading,character recognition,feature learning,photo OCR},
pages = {440--445},
title = {{Text detection and character recognition in scene images with unsupervised feature learning}},
year = {2011}
@article{Elagouni2012,
author = {Elagouni, Khaoula and Garcia, Christophe and Mamalet, Franck and S{\'{e}}billot, Pascale},
doi = {10.1007/978-3-642-33266-1_22},
isbn = {9783642332654},
issn = {03029743},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {CTC,ConvNet,LSTM,Video text recognition,multi-scale image scanning},
number = {PART 2},
pages = {172--179},
title = {{Text recognition in videos using a recurrent connectionist approach}},
volume = {7553 LNCS},
year = {2012}
@article{Sermanet2012,
archivePrefix = {arXiv},
arxivId = {1204.3968},
author = {Sermanet, Pierre and Chintala, Soumith and Lecun, Yann},
eprint = {1204.3968},
isbn = {9784990644109},
issn = {10514651},
journal = {Proceedings - International Conference on Pattern Recognition},
pages = {3288--3291},
title = {{Convolutional neural networks applied to house numbers digit classification}},
year = {2012}
@article{Zhang2013,
archivePrefix = {arXiv},
arxivId = {arXiv:1711.04249v1},
author = {Zhang, Sheng and Liu, Yuliang and Jin, Lianwen and Luo, Canjie},
eprint = {arXiv:1711.04249v1},
title = {{Feature Enhancement Network: A Refined Scene Text Detector}},
year = {2013}
@article{Mishra2013,
author = {Mishra, Anand and Alahari, Karteek and Jawahar, C V},
pages = {1--11},
title = {{Scene Text Recognition using Higher Order Language Priors To cite this version : Scene Text Recognition using Higher Order Language Priors}},
year = {2013}
@article{Bissacco2013,
author = {Bissacco, Alessandro and Cummins, Mark and Netzer, Yuval and Neven, Hartmut},
doi = {10.1109/ICCV.2013.102},
isbn = {9781479928392},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
keywords = {OCR,deep learning,scene text,text recognition},
pages = {785--792},
title = {{PhotoOCR: Reading text in uncontrolled conditions}},
year = {2013}
@article{Gonzalez2013,
author = {Gonz{\'{a}}lez, {\'{A}}lvaro and Bergasa, Luis Miguel},
doi = {10.1016/j.imavis.2013.01.003},
issn = {02628856},
journal = {Image and Vision Computing},
keywords = {Character recognition,Character segmentation,Natural images,Scene text detection,Text detection,Text recognition},
number = {3},
pages = {255--274},
title = {{A text reading algorithm for natural images}},
volume = {31},
year = {2013}
@article{Bakhti2013,
author = {Bakhti, Mostafa and Snaidero, Nicolas and Schneider, David and Aggarwal, Shweta and M{\"{o}}bius, Wiebke and Janshoff, Andreas and Eckhardt, Matthias and Nave, Klaus Armin and Simons, Mikael},
doi = {10.1073/pnas.1220104110},
issn = {00278424},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Adhesiveness,Myelination,PLP},
number = {8},
pages = {3143--3148},
pmid = {23382229},
title = {{Loss of electrostatic cell-surface repulsion mediates myelin membrane adhesion and compaction in the central nervous system}},
volume = {110},
year = {2013}
@article{Graves2014,
author = {Graves, Alex and Jaitly, Navdeep},
isbn = {9781634393973},
journal = {31st International Conference on Machine Learning, ICML 2014},
pages = {3771--3779},
title = {{Towards end-to-end speech recognition with recurrent neural networks}},
volume = {5},
year = {2014}
@article{Bluche2014,
author = {Bluche, Th{\'{e}}odore and Ney, Hermann and Kermorvant, Christopher},
doi = {10.1007/978-3-319-11397-5_15},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Deep neural networks,Handwriting recognition,Recurrent neural networks},
pages = {199--219},
title = {{A comparison of sequence-trained deep neural networks and recurrent neural networks optical modeling for handwriting recognition}},
volume = {8791},
year = {2014}
@article{Liu2015,
author = {Liu, Yuliang and Jin, Lianwen},
pages = {1962--1969},
title = {{Deep Matching Prior Network : Toward Tighter Multi-oriented Text Detection}},
year = {2015}
@article{He2015,
author = {He, Dafang and Yang, Xiao and Liang, Chen and Zhou, Zihan and Ororbia, Alex G and Kifer, Daniel and Giles, C Lee},
number = {1},
title = {{Multi-scale FCN with Cascaded Instance Aware Segmentation for Arbitrary Oriented Word Spotting In The Wild}},
year = {2015}
@article{Sahu2015,
archivePrefix = {arXiv},
arxivId = {1511.04176},
author = {Sahu, Devendra Kumar and Sukhwani, Mohak},
eprint = {1511.04176},
pages = {1--9},
title = {{Sequence to Sequence Learning for Optical Character Recognition}},
url = {http://arxiv.org/abs/1511.04176},
year = {2015}
@article{Wang2015,
archivePrefix = {arXiv},
arxivId = {1507.03196},
author = {Wang, Zhangyang and Yang, Jianchao and Jin, Hailin and Shechtman, Eli and Agarwala, Aseem and Brandt, Jonathan and Huang, Thomas S.},
doi = {10.1145/2733373.2806219},
eprint = {1507.03196},
isbn = {9781450334594},
journal = {MM 2015 - Proceedings of the 2015 ACM Multimedia Conference},
keywords = {Deep Learning,Domain Adaptation,Model Compression,Visual Font Recognition},
pages = {451--459},
title = {{DeepFont: Identify your font from an image}},
year = {2015}
@article{Visin2015,
archivePrefix = {arXiv},
arxivId = {1505.00393},
author = {Visin, Francesco and Kastner, Kyle and Cho, Kyunghyun and Matteucci, Matteo and Courville, Aaron and Bengio, Yoshua},
eprint = {1505.00393},
pages = {1--9},
title = {{ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks}},
url = {http://arxiv.org/abs/1505.00393},
year = {2015}
@article{Yousef2015,
archivePrefix = {arXiv},
arxivId = {arXiv:1812.11894v1},
author = {Yousef, Mohamed and Hussain, Khaled F and Mohammed, Usama S},
eprint = {arXiv:1812.11894v1},
number = {8},
pages = {1--13},
title = {{Accurate , Data-Efficient , Unconstrained Text Recognition with Convolutional Neural Networks}},
volume = {14},
year = {2015}
@article{Jaderberg2015,
archivePrefix = {arXiv},
arxivId = {1412.5903},
author = {Jaderberg, Max and Simonyan, Karen and Vedaldi, Andrea and Zisserman, Andrew},
eprint = {1412.5903},
journal = {3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings},
pages = {1--10},
title = {{Deep structured output learning for unconstrained text recognition}},
year = {2015}
@article{Su2015,
author = {Su, Bolan and Lu, Shijian},
doi = {10.1007/978-3-319-16865-4_3},
isbn = {9783319168647},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {35--48},
title = {{Accurate scene text recognition based on recurrent neural network}},
volume = {9003},
year = {2015}
@article{Sønderby2015,
archivePrefix = {arXiv},
arxivId = {1509.05329},
author = {S{\o}nderby, S{\o}ren Kaae and S{\o}nderby, Casper Kaae and Maal{\o}e, Lars and Winther, Ole},
eprint = {1509.05329},
title = {{Recurrent Spatial Transformer Networks}},
url = {http://arxiv.org/abs/1509.05329},
year = {2015}
@article{Jaderberg2015a,
archivePrefix = {arXiv},
arxivId = {1506.02025},
author = {Jaderberg, Max and Simonyan, Karen and Zisserman, Andrew and Kavukcuoglu, Koray},
eprint = {1506.02025},
issn = {10495258},
journal = {Advances in Neural Information Processing Systems},
pages = {2017--2025},
title = {{Spatial transformer networks}},
volume = {2015-Janua},
year = {2015}
@article{Zhang2016,
archivePrefix = {arXiv},
arxivId = {1604.04018},
author = {Zhang, Zheng and Zhang, Chengquan and Shen, Wei and Yao, Cong and Liu, Wenyu and Bai, Xiang},
doi = {10.1109/CVPR.2016.451},
eprint = {1604.04018},
isbn = {9781467388504},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {4159--4167},
title = {{Multi-oriented Text Detection with Fully Convolutional Networks}},
volume = {2016-December},
year = {2016}
@article{Yao2016,
archivePrefix = {arXiv},
arxivId = {1606.09002},
author = {Yao, Cong and Bai, Xiang and Sang, Nong and Zhou, Xinyu and Zhou, Shuchang and Cao, Zhimin},
eprint = {1606.09002},
keywords = {fully convolutional network,holistic prediction,natural images,scene text detection},
pages = {1--10},
title = {{Scene Text Detection via Holistic, Multi-Channel Prediction}},
url = {http://arxiv.org/abs/1606.09002},
year = {2016}
@article{He2016a,
archivePrefix = {arXiv},
arxivId = {1506.04395},
author = {He, Pan and Huang, Weilin and Qiao, Yu and Loy, Chen Change and Tang, Xiaoou},
eprint = {1506.04395},
isbn = {9781577357605},
journal = {30th AAAI Conference on Artificial Intelligence, AAAI 2016},
pages = {3501--3508},
title = {{Reading scene text in deep convolutional sequences}},
year = {2016}
@article{Yang2016,
archivePrefix = {arXiv},
arxivId = {1611.07385},
author = {Yang, Xiao and He, Dafang and Huang, Wenyi and Zhou, Zihan and Ororbia, Alex and Kifer, Dan and Giles, C. Lee},
eprint = {1611.07385},
number = {2010},
title = {{Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading}},
url = {http://arxiv.org/abs/1611.07385},
year = {2016}
@article{Gomez2016,
archivePrefix = {arXiv},
arxivId = {1407.7504},
author = {Gomez, Lluis and Karatzas, Dimosthenis},
doi = {10.1007/s10032-016-0274-2},
eprint = {1407.7504},
issn = {14332825},
journal = {International Journal on Document Analysis and Recognition},
keywords = {Detection,Hierarchical grouping,Perceptual organization,Scene text,Segmentation},
number = {4},
pages = {335--349},
title = {{A fast hierarchical method for multi-script and arbitrary oriented scene text extraction}},
volume = {19},
year = {2016}
@article{Shi2016a,
archivePrefix = {arXiv},
arxivId = {1603.03915},
author = {Shi, Baoguang and Wang, Xinggang and Lyu, Pengyuan and Yao, Cong and Bai, Xiang},
doi = {10.1109/CVPR.2016.452},
eprint = {1603.03915},
isbn = {9781467388504},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {4168--4176},
title = {{Robust Scene Text Recognition with Automatic Rectification}},
volume = {2016-Decem},
year = {2016}
@article{Lee2016a,
archivePrefix = {arXiv},
arxivId = {1603.03101},
author = {Lee, Chen Yu and Osindero, Simon},
doi = {10.1109/CVPR.2016.245},
eprint = {1603.03101},
isbn = {9781467388504},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
number = {3},
pages = {2231--2239},
title = {{Recursive Recurrent Nets with Attention Modeling for OCR in the Wild}},
volume = {2016-December},
year = {2016}
@article{He2016b,
archivePrefix = {arXiv},
arxivId = {1506.04395},
author = {He, Pan and Huang, Weilin and Qiao, Yu and Loy, Chen Change and Tang, Xiaoou},
eprint = {1506.04395},
isbn = {9781577357605},
journal = {30th AAAI Conference on Artificial Intelligence, AAAI 2016},
pages = {3501--3508},
title = {{Reading scene text in deep convolutional sequences}},
year = {2016}
@article{He2016,
archivePrefix = {arXiv},
arxivId = {1510.03283},
author = {He, Tong and Huang, Weilin and Qiao, Yu and Yao, Jian},
doi = {10.1109/TIP.2016.2547588},
eprint = {1510.03283},
issn = {10577149},
journal = {IEEE Transactions on Image Processing},
keywords = {Maximally stable extremal regions,convolutional neural networks,multi-level supervised information,multi-task learning,text detector},
number = {6},
pages = {2529--2541},
pmid = {27093723},
title = {{Text-Attentional Convolutional Neural Network for Scene Text Detection}},
volume = {25},
year = {2016}
@article{Shi2016,
archivePrefix = {arXiv},
arxivId = {1603.03915},
author = {Shi, Baoguang and Wang, Xinggang and Lyu, Pengyuan and Yao, Cong and Bai, Xiang},
doi = {10.1109/CVPR.2016.452},
eprint = {1603.03915},
isbn = {9781467388504},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {4168--4176},
title = {{Robust Scene Text Recognition with Automatic Rectification}},
volume = {2016-Decem},
year = {2016}
@article{Liu2016,
author = {Liu, Wei and Chen, Chaofeng and Wong, Kwan Yee K. and Su, Zhizhong and Han, Junyu},
doi = {10.5244/C.30.43},
journal = {British Machine Vision Conference 2016, BMVC 2016},
pages = {43.1--43.13},
title = {{STAR-Net: A spatial attention residue network for scene text recognition}},
volume = {2016-Septe},
year = {2016}
@article{Jaderberg2016,
archivePrefix = {arXiv},
arxivId = {1412.1842},
author = {Jaderberg, Max and Simonyan, Karen and Vedaldi, Andrea and Zisserman, Andrew},
doi = {10.1007/s11263-015-0823-z},
eprint = {1412.1842},
issn = {15731405},
journal = {International Journal of Computer Vision},
keywords = {Convolutional neural networks,Deep learning,Synthetic data,Text detection,Text recognition,Text retrieval,Text spotting},
number = {1},
pages = {1--20},
title = {{Reading Text in the Wild with Convolutional Neural Networks}},
volume = {116},
year = {2016}
@article{Lee2016,
archivePrefix = {arXiv},
arxivId = {1603.03101},
author = {Lee, Chen Yu and Osindero, Simon},
doi = {10.1109/CVPR.2016.245},
eprint = {1603.03101},
isbn = {9781467388504},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
number = {3},
pages = {2231--2239},
title = {{Recursive Recurrent Nets with Attention Modeling for OCR in the Wild}},
volume = {2016-Decem},
year = {2016}
@article{Veit2016,
archivePrefix = {arXiv},
arxivId = {1601.07140},
author = {Veit, Andreas and Matera, Tomas and Neumann, Lukas and Matas, Jiri and Belongie, Serge},
eprint = {1601.07140},
title = {{COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images}},
url = {http://arxiv.org/abs/1601.07140},
year = {2016}
@inproceedings{Dong2017,
author = {Dong, Meng and He, Dongliang and Luo, Chong and Liu, Dong and Zeng, Wenjun},
booktitle = {British Machine Vision Conference 2017, BMVC 2017},
doi = {10.5244/c.31.175},
isbn = {190172560X},
pages = {1--12},
title = {{A CNn-based approach for automatic license plate recognition in the wild}},
year = {2017}
@misc{Zhu2017,
author = {Zhu, Xiangyu and Jiang, Yingying and Yang, Shuli and Wang, Xiaobing and Li, Wei and Fu, Pei and Wang, Hua and Luo, Zhenbo},
booktitle = {arXiv},
keywords = {CTPN,Deep Residual Networks,Scene text detection},
number = {1},
title = {{Deep residual text detection network for scene text}},
year = {2017}
@misc{Zhu2017a,
author = {Zhu, Xiangyu and Jiang, Yingying and Yang, Shuli and Wang, Xiaobing and Li, Wei and Fu, Pei and Wang, Hua and Luo, Zhenbo},
booktitle = {arXiv},
keywords = {CTPN,Deep Residual Networks,Scene text detection},
pages = {7269--7278},
title = {{Deep residual text detection network for scene text}},
year = {2017}
@misc{Sain2017,
author = {Sain, Aneeshan and Bhunia, Ayan Kumar and Roy, Partha Pratim and Pal, Umapada},
booktitle = {arXiv},
keywords = {Fouier-Laplacian,Hidden Markov Model,Scene text and Video text retrieval,Skeletonization.1,Text extraction},
title = {{Multi-oriented text detection and verification in video frames and scene images}},
year = {2017}
@article{Zhong2017,
author = {Zhong, Zhuoyao and Sun, Lei and Huo, Qiang},
title = {{An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches Anchor-free Region Proposal Network}},
year = {2017}
@article{Bai2017,
author = {Bai, Xiang and Shi, Baoguang and Zhang, Chengquan and Cai, Xuan and Qi, Li},
doi = {10.1016/j.patcog.2016.12.005},
number = {March 2016},
pages = {437--446},
title = {{Text / non-text image classi fi cation in the wild with convolutional neural networks}},
volume = {66},
year = {2017}
@article{He2017,
archivePrefix = {arXiv},
arxivId = {1709.00138},
author = {He, Pan and Huang, Weilin and He, Tong and Zhu, Qile and Qiao, Yu and Li, Xiaolin},
doi = {10.1109/ICCV.2017.331},
eprint = {1709.00138},
isbn = {9781538610329},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {3066--3074},
title = {{Single Shot Text Detector with Regional Attention}},
volume = {2017-Octob},
year = {2017}
@article{Liao2017,
archivePrefix = {arXiv},
arxivId = {1611.06779},
author = {Liao, Minghui and Shi, Baoguang and Bai, Xiang and Wang, Xinggang and Liu, Wenyu},
eprint = {1611.06779},
journal = {31st AAAI Conference on Artificial Intelligence, AAAI 2017},
pages = {4161--4167},
title = {{TextBoxes: A fast text detector with a single deep neural network}},
year = {2017}
@article{Zhan2017,
archivePrefix = {arXiv},
arxivId = {1710.03112},
author = {Zhan, Hongjian and Wang, Qingqing and Lu, Yue},
doi = {10.1007/978-3-319-70136-3_62},
eprint = {1710.03112},
isbn = {9783319701356},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Connectionist temporal classification,Convolutional neural network,Digit string recognition,End to end,Recurrent neural network},
pages = {583--591},
title = {{Handwritten digit string recognition by combination of residual network and RNN-CTC}},
volume = {10639 LNCS},
year = {2017}
@article{Wojna2017,
archivePrefix = {arXiv},
arxivId = {1704.03549},
author = {Wojna, Zbigniew and Gorban, Alexander N. and Lee, Dar Shyang and Murphy, Kevin and Yu, Qian and Li, Yeqing and Ibarz, Julian},
doi = {10.1109/ICDAR.2017.143},
eprint = {1704.03549},
isbn = {9781538635865},
issn = {15205363},
journal = {Proceedings of the International Conference on Document Analysis and Recognition, ICDAR},
pages = {844--850},
title = {{Attention-Based Extraction of Structured Information from Street View Imagery}},
volume = {1},
year = {2017}
@article{Bluche2017,
archivePrefix = {arXiv},
arxivId = {1604.03286},
author = {Bluche, Theodore and Louradour, Jeroome and Messina, Ronaldo},
doi = {10.1109/ICDAR.2017.174},
eprint = {1604.03286},
isbn = {9781538635865},
issn = {15205363},
journal = {Proceedings of the International Conference on Document Analysis and Recognition, ICDAR},
pages = {1050--1055},
title = {{Scan, Attend and Read: End-To-End Handwritten Paragraph Recognition with MDLSTM Attention}},
volume = {1},
year = {2017}
@article{He2017a,
author = {He, Wenhao and Fei, Xu-yao Zhang and Liu, Yin Cheng-lin},
journal = {Iccv},
pages = {745--753},
title = {{Deep Direct Regression for Multi-Oriented Scene Text Detection National Laboratory of Pattern Recognition ( NLPR )}},
year = {2017}
@article{Jiang2017,
archivePrefix = {arXiv},
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journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Character recognition,Decoding,Detectors,Image Transformation,Proposals,Recurrent neural networks,Scene Text Recognition,Sequence-to-Sequence Learning,Text recognition,Thin-Plate Spline},
number = {c},
pages = {1},
pmid = {29994467},
publisher = {IEEE},
title = {{ASTER: An Attentional Scene Text Recognizer with Flexible Rectification}},
volume = {PP},
year = {2018}
@article{Lyu2018a,
archivePrefix = {arXiv},
arxivId = {1908.08207},
author = {Lyu, Pengyuan and Liao, Minghui and Yao, Cong and Wu, Wenhao and Bai, Xiang},
doi = {10.1007/978-3-030-01264-9_5},
eprint = {1908.08207},
isbn = {9783030012632},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Arbitrary shapes,Neural network,Scene text spotting},
pages = {71--88},
title = {{Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes}},
volume = {11218 LNCS},
year = {2018}
@article{He2018a,
archivePrefix = {arXiv},
arxivId = {1803.03474},
author = {He, Tong and Tian, Zhi and Huang, Weilin and Shen, Chunhua and Qiao, Yu and Sun, Changming},
doi = {10.1109/CVPR.2018.00527},
eprint = {1803.03474},
isbn = {9781538664209},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {5020--5029},
title = {{An End-to-End TextSpotter with Explicit Alignment and Attention}},
year = {2018}
@article{Chen2018,
author = {Chen, Boyo and Chen, Buo Fu and Lin, Hsuan Tien},
doi = {10.1145/3219819.3219926},
isbn = {9781450355520},
journal = {Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
keywords = {Atmospheric Science,Blending,Convolutional Neural Network,Dropout,Pooling,Regression,Tropical cyclone,Tropical cyclone intensity},
pages = {90--99},
title = {{Rotation-blended CNNs on a new open dataset for tropical cyclone image-to-intensity regression}},
year = {2018}
@article{Gao2018,
author = {Gao, Ge and Lauri, Mikko and Zhang, Jianwei and Frintrop, Simone},
journal = {arXiv},
keywords = {6D pose estimation,Convolutional neural network,Lie algebra,Point cloud},
title = {{Occlusion resistant object rotation regression from point cloud segments}},
year = {2018}
@article{Sabir2018,
archivePrefix = {arXiv},
arxivId = {1805.09441},
author = {Sabir, Ekraam and Rawls, Stephen and Natarajan, Prem},
doi = {10.1109/ICDAR.2017.361},
eprint = {1805.09441},
isbn = {9781538635865},
issn = {15205363},
journal = {Proceedings of the International Conference on Document Analysis and Recognition, ICDAR},
keywords = {LSTM,Language Model,OCR},
number = {implicit LM},
pages = {27--31},
title = {{Implicit Language Model in LSTM for OCR}},
volume = {7},
year = {2018}
@article{Lyu2018b,
archivePrefix = {arXiv},
arxivId = {1908.08207},
author = {Lyu, Pengyuan and Liao, Minghui and Yao, Cong and Wu, Wenhao and Bai, Xiang},
doi = {10.1007/978-3-030-01264-9_5},
eprint = {1908.08207},
isbn = {9783030012632},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Arbitrary shapes,Neural network,Scene text spotting},
pages = {71--88},
title = {{Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes}},
volume = {11218 LNCS},
year = {2018}
@article{Zhan2018a,
archivePrefix = {arXiv},
arxivId = {1807.03021},
author = {Zhan, Fangneng and Lu, Shijian and Xue, Chuhui},
doi = {10.1007/978-3-030-01237-3_16},
eprint = {1807.03021},
isbn = {9783030012366},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Data augmentation,Image synthesis,Scene text detection,Scene text recognition},
pages = {257--273},
title = {{Verisimilar image synthesis for accurate detection and recognition of texts in scenes}},
volume = {11212 LNCS},
year = {2018}
@inproceedings{Long2018a,
archivePrefix = {arXiv},
arxivId = {1807.01544},
author = {Long, Shangbang and Ruan, Jiaqiang and Zhang, Wenjie and He, Xin and Wu, Wenhao and Yao, Cong},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-030-01216-8_2},
eprint = {1807.01544},
isbn = {9783030012151},
issn = {16113349},
keywords = {Curved text,Deep neural network,Scene text detection},
pages = {19--35},
title = {{TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes}},
volume = {11206 LNCS},
year = {2018}
@article{Gao2018a,
archivePrefix = {arXiv},
arxivId = {1808.00677},
author = {Gao, Yuting and Huang, Zheng and Dai, Yuchen},
eprint = {1808.00677},
journal = {arXiv},
keywords = {Scene text recognition},
title = {{DSAN: Double supervised network with attention mechanism for scene text recognition}},
year = {2018}
@article{Borisyuk2018,
archivePrefix = {arXiv},
arxivId = {1910.05085},
author = {Borisyuk, Fedor and Gordo, Albert and Sivakumar, Viswanath},
doi = {10.1145/3219819.3219861},
eprint = {1910.05085},
isbn = {9781450355520},
journal = {Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
keywords = {Optical character recognition,Text detection,Text recognition},
pages = {71--79},
title = {{Rosetta: Large scale system for text detection and recognition in images}},
year = {2018}
@article{Roy2018,
archivePrefix = {arXiv},
arxivId = {1804.06254},
author = {Roy, Partha Pratim and Mohta, Akash and Chaudhuri, Bidyut B.},
eprint = {1804.06254},
journal = {arXiv},
keywords = {Hidden Markov Models,Indic Text Recognition,Synthetic Data Generation},
pages = {1--35},
title = {{Synthetic data generation for Indic handwritten text recognition}},
year = {2018}
@article{Zhang2019,
archivePrefix = {arXiv},
arxivId = {1904.06535},
author = {Zhang, Chengquan and Liang, Borong and Huang, Zuming and En, Mengyi and Han, Junyu and Ding, Errui and Ding, Xinghao},
doi = {10.1109/CVPR.2019.01080},
eprint = {1904.06535},
isbn = {9781728132938},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
keywords = {Categorization,Document Analysis,Grouping and Shape,Recognition: Detection,Retrieval,Segmentation,Vision Applicat},
pages = {10544--10553},
title = {{Look more than once: An accurate detector for text of arbitrary shapes}},
volume = {2019-June},
year = {2019}
@inproceedings{Liu2019,
archivePrefix = {arXiv},
arxivId = {1903.08836},
author = {Liu, Zichuan and Lin, Guosheng and Yang, Sheng and Liu, Fayao and Lin, Weisi and Goh, Wang Ling},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2019.00744},
eprint = {1903.08836},
isbn = {9781728132938},
issn = {10636919},
keywords = {Categorization,Recognition: Detection,Retrieval},
pages = {7261--7270},
title = {{Towards robust curve text detection with conditional spatial expansion}},
volume = {2019-June},
year = {2019}
@inproceedings{Liu2019a,
archivePrefix = {arXiv},
arxivId = {1903.08836},
author = {Liu, Zichuan and Lin, Guosheng and Yang, Sheng and Liu, Fayao and Lin, Weisi and Goh, Wang Ling},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2019.00744},
eprint = {1903.08836},
isbn = {9781728132938},
issn = {10636919},
keywords = {Categorization,Recognition: Detection,Retrieval},
pages = {7261--7270},
title = {{Towards robust curve text detection with conditional spatial expansion}},
volume = {2019-June},
year = {2019}
@inproceedings{Zhang2019a,
archivePrefix = {arXiv},
arxivId = {1904.06535},
author = {Zhang, Chengquan and Liang, Borong and Huang, Zuming and En, Mengyi and Han, Junyu and Ding, Errui and Ding, Xinghao},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2019.01080},
eprint = {1904.06535},
isbn = {9781728132938},
issn = {10636919},
keywords = {Categorization,Document Analysis,Grouping and Shape,Recognition: Detection,Retrieval,Segmentation,Vision Applicat},
pages = {10544--10553},
title = {{Look more than once: An accurate detector for text of arbitrary shapes}},
volume = {2019-June},
year = {2019}
@inproceedings{Zhang2019b,
archivePrefix = {arXiv},
arxivId = {1904.06535},
author = {Zhang, Chengquan and Liang, Borong and Huang, Zuming and En, Mengyi and Han, Junyu and Ding, Errui and Ding, Xinghao},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2019.01080},
eprint = {1904.06535},
isbn = {9781728132938},
issn = {10636919},
keywords = {Categorization,Document Analysis,Grouping and Shape,Recognition: Detection,Retrieval,Segmentation,Vision Applicat},
pages = {10544--10553},
title = {{Look more than once: An accurate detector for text of arbitrary shapes}},
volume = {2019-June},
year = {2019}
@inproceedings{Zhang2019c,
archivePrefix = {arXiv},
arxivId = {1904.06535},
author = {Zhang, Chengquan and Liang, Borong and Huang, Zuming and En, Mengyi and Han, Junyu and Ding, Errui and Ding, Xinghao},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2019.01080},
eprint = {1904.06535},
isbn = {9781728132938},
issn = {10636919},
keywords = {Categorization,Document Analysis,Grouping and Shape,Recognition: Detection,Retrieval,Segmentation,Vision Applicat},
pages = {10544--10553},
title = {{Look more than once: An accurate detector for text of arbitrary shapes}},
volume = {2019-June},
year = {2019}
@article{Jiao2019,
author = {Jiao, Licheng and Zhang, F A N and Liu, Fang and Member, Senior},
doi = {10.1109/ACCESS.2019.2939201},
journal = {IEEE Access},
pages = {128837--128868},
publisher = {IEEE},
title = {{A Survey of Deep Learning-Based Object Detection}},
volume = {7},
year = {2019}
@article{Ch2019,
author = {Ch, Chee-kheng and Chan, Chee Seng and Liu, Cheng-lin},
doi = {10.1007/s10032-019-00334-z},
issn = {1433-2825},
journal = {International Journal on Document Analysis and Recognition (IJDAR)},
keywords = {Curved text,Scene text detection,curved text,scene text detection},
publisher = {Springer Berlin Heidelberg},
title = {{Total-Text : toward orientation robustness in scene text detection}},
url = {https://doi.org/10.1007/s10032-019-00334-z},
year = {2019}
@article{Deng2019,
archivePrefix = {arXiv},
arxivId = {arXiv:1804.02690v2},
author = {Deng, Linjie and Gong, Yanxiang and Lin, Yi and Shuai, Jingwen and Tu, Xiaoguang and Zhang, Yuefei and Ma, Zheng and Xie, Mei},
eprint = {arXiv:1804.02690v2},
keywords = {corner-based region proposal network,dual-roi pooling,multi-oriented text detection},
title = {{Detecting Multi-Oriented Text with Corner-based Region Proposals}},
year = {2019}
@article{Wang2019,
author = {Wang, Wenhai and Xie, Enze and Song, Xiaoge and Zang, Yuhang and Wang, Wenjia and Lu, Tong and Yu, Gang and Shen, Chunhua},
journal = {Iccv2019},
pages = {8440--8449},
title = {{Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network National Key Lab for Novel Software Technology , Nanjing University University of Electronic Science and Technology of China The University of Adelaide}},
year = {2019}
@article{Zhang2019d,
author = {Zhang, Chengquan and Liang, Borong and Huang, Zuming and En, Mengyi and Han, Junyu and Ding, Errui and Ding, Xinghao},
journal = {arXiv},
title = {{Look more than once: An accurate detector for text of arbitrary shapes}},
volume = {1},
year = {2019}
@book{Liu2019b,
author = {Liu, Chao and Zou, Yuexian and Yang, Dongming},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-030-05710-7_44},
isbn = {9783030057091},
issn = {16113349},
keywords = {Attention mechanism,Hierarchical feature fusion,Scene text detection (STD),Semantic segmentation},
pages = {531--542},
publisher = {Springer International Publishing},
title = {{Enhancing Scene Text Detection via Fused Semantic Segmentation Network with Attention}},
url = {http://dx.doi.org/10.1007/978-3-030-05710-7{\_}44},
volume = {11295 LNCS},
year = {2019}
@article{Conceicao2019,
author = {Concei{\c{c}}{\~{a}}o, Jhonatas Santos de Jesus and Pinto, Allan and Decker, Luis and Campana, Jose Luis Flores and Neira, Manuel Cordova and {Dos Santos}, Andrezza A. and Pedrini, Helio and Torres, Ricardo},
doi = {10.5753/sibgrapi.est.2019.8333},
pages = {215--218},
title = {{Multi-Lingual Text Localization via Language-Specific Convolutional Neural Networks}},
year = {2019}
@article{Liu2019c,
archivePrefix = {arXiv},
arxivId = {1903.11800},
author = {Liu, Jingchao and Liu, Xuebo and Sheng, Jie and Liang, Ding and Li, Xin and Liu, Qingjie},
eprint = {1903.11800},
journal = {arXiv},
title = {{Pyramid mask text detector}},
year = {2019}
@article{Xing2019,
author = {Xing, Linjie and Tian, Zhi and Huang, Weilin and Scott, Matthew R.},
journal = {arXiv},
pages = {9126--9136},
title = {{Convolutional character networks}},
year = {2019}
@article{Feng2019,
author = {Feng, Wei and He, Wenhao and Yin, Fei and Zhang, Xu Yao and Liu, Cheng Lin},
doi = {10.1109/ICCV.2019.00917},
isbn = {9781728148038},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {9075--9084},
title = {{Textdragon: An end-to-end framework for arbitrary shaped text spotting}},
volume = {2019-Octob},
year = {2019}
@article{Wang2019a,
author = {Wang, Xiaobing and Jiang, Yingying and Luo, Zhenbo and Liu, Cheng Lin and Choi, Hyunsoo and Kim, Sungjin},
doi = {10.1109/CVPR.2019.00661},
isbn = {9781728132938},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
keywords = {Categorization,Recognition: Detection,Retrieval,Vision Applications and Systems},
pages = {6442--6451},
title = {{Arbitrary shape scene text detection with adaptive text region representation}},
volume = {2019-June},
year = {2019}
@article{Li2019,
archivePrefix = {arXiv},
arxivId = {1709.08828},
author = {Li, Hui and Wang, Peng and Shen, Chunhua},
doi = {10.1109/TITS.2018.2847291},
eprint = {1709.08828},
issn = {15249050},
journal = {IEEE Transactions on Intelligent Transportation Systems},
keywords = {Car plate detection and recognition,convolutional neural networks,recurrent neural networks},
number = {3},
pages = {1126--1136},
title = {{Toward End-to-End Car License Plate Detection and Recognition with Deep Neural Networks}},
volume = {20},
year = {2019}
@article{Series2019,
author = {Series, Conference},
doi = {10.1088/1742-6596/1314/1/012200},
title = {{Natural Scene Chinese Character Text Detection Method Based on Improved CTPN Natural Scene Chinese Character Text Detection Method Based on Improved CTPN}},
year = {2019}
@article{Zhan2019,
archivePrefix = {arXiv},
arxivId = {1812.05824},
author = {Zhan, Fangneng and Lu, Shijian},
doi = {10.1109/CVPR.2019.00216},
eprint = {1812.05824},
isbn = {9781728132938},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
keywords = {Categorization,Deep Learning,Document Analysis,Recognition: Detection,Retrieval},
pages = {2054--2063},
title = {{ESIR: End-to-end scene text recognition via iterative image rectification}},
volume = {2019-June},
year = {2019}
@article{Goel2019,
author = {Goel, Vaibhav and Kumar, Vaibhav and Jaggi, Amandeep Singh and Nagrath, Preeti},
doi = {10.5815/ijitcs.2019.09.06},
number = {September},
pages = {48--54},
title = {{Text Extraction from Natural Scene Images using OpenCV and CNN}},
year = {2019}
@article{Work2019,
author = {Work, Elated},
number = {2015},
pages = {1--15},
title = {{O BJECT DETECTION DEEP LEARNING}},
year = {2019}
@article{NguyenVan2019,
archivePrefix = {arXiv},
arxivId = {1811.10003},
author = {NguyenVan, Dinh and Lu, Shijian and Tian, Shangxuan and Ouarti, Nizar and Mokhtari, Mounir},
doi = {10.1016/j.patcog.2018.10.012},
eprint = {1811.10003},
issn = {00313203},
journal = {Pattern Recognition},
keywords = {Pooling based grouping,Scene text detection,Scene text proposal,Scene text reading,Scene text spotting},
pages = {118--129},
title = {{A pooling based scene text proposal technique for scene text reading in the wild}},
volume = {87},
year = {2019}
@article{Blanco-medina2019,
author = {Blanco-medina, Pablo and Fidalgo, Eduardo and Alegre, Enrique and Al-nabki, Mhd Wesam and Chaves, Deisy},
isbn = {9788497497169},
keywords = {10,11,and counterfeiting personal,cybersecurity,drugs trading,identification documents,ing,ocr,text recognition,text spotting,tor darknet},
pages = {828--835},
title = {{ENHANCING TEXT RECOGNITION ON TOR DARKNET}},
year = {2019}
@article{Singh2019,
human-in-the-loop, is an interesting problem for surveillance and other
similar applications. Achieving high accuracy while reading license
plates in the real world videos is cumbersome due to complexities like
multiple vehicles, high-density traffic in spatial and temporal domains,
varying camera angles and illumination, occlusions and multiple
resolutions. We present a modular framework for OCR corrections in the
chaotic Indian traffic videos that especially involve complex license
plate patterns. Such patterns are obtained from a state-of-the-art deep
learning model trained on video frames. Since such a model reads the
text from videos (instead of images), we incorporate multi-frame
consensus for generating suggestions in our framework. To ease the
correction process, our human-interactive framework first breaks down
the multi-vehicle videos into multiple clips, each containing a single
vehicle from the video using an object detector and a tracker. Our
framework then provides suggestions for an individual vehicle using
multi-frame consensus. Our framework then selectively presents these
extracted clips to the user to verify/correct the predictions with
minimal human efforts via interactive suggestions. Such high-quality
output can be used to continuously update a large database for
surveillance and can be further used to improve the accuracy of deep
models in the complex real-world scenarios.},
author = {Singh, Pankaj and Patwa, Bhavya and Saluja, Rohit and Ramakrishnan, Ganesh and Chaudhuri, Parag},
doi = {10.1109/icdarw.2019.10036},
pages = {36--40},
title = {{StreetOCRCorrect: An Interactive Framework for OCR Corrections in Chaotic Indian Street Videos}},
year = {2019}
@article{Luo2019,
archivePrefix = {arXiv},
arxivId = {1901.03003},
author = {Luo, Canjie and Jin, Lianwen and Sun, Zenghui},
doi = {10.1016/j.patcog.2019.01.020},
eprint = {1901.03003},
issn = {00313203},
journal = {Pattern Recognition},
keywords = {Deep learning,Optical character recognition,Scene text recognition},
pages = {109--118},
title = {{MORAN: A Multi-Object Rectified Attention Network for scene text recognition}},
volume = {90},
year = {2019}
@article{Wang2019b,
archivePrefix = {arXiv},
arxivId = {1903.12473},
author = {Wang, Wenhai and Xie, Enze and Li, Xiang and Hou, Wenbo and Lu, Tong and Yu, Gang and Shao, Shuai},
doi = {10.1109/CVPR.2019.00956},
eprint = {1903.12473},
isbn = {9781728132938},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
keywords = {Categorization,Deep Learning,Recognition: Detection,Retrieval},
number = {c},
pages = {9328--9337},
title = {{Shape robust text detection with progressive scale expansion network}},
volume = {2019-June},
year = {2019}
@article{Cheng2019,
archivePrefix = {arXiv},
arxivId = {1906.09731},
author = {Cheng, Zhengxue and Sun, Heming and Takeuchi, Masaru and Katto, Jiro},
eprint = {1906.09731},
journal = {arXiv},
title = {{Deep residual learning for image compression}},
year = {2019}
@article{Vuola2019,
archivePrefix = {arXiv},
arxivId = {1901.10170},
author = {Vuola, Aarno Oskar and Akram, Saad Ullah and Kannala, Juho},
doi = {10.1109/ISBI.2019.8759574},
eprint = {1901.10170},
isbn = {9781538636411},
issn = {19458452},
journal = {Proceedings - International Symposium on Biomedical Imaging},
keywords = {Convolutional neural networks,Microscopy image analysis,Nuclei segmentation},
pages = {208--212},
title = {{Mask-RCNN and u-net ensembled for nuclei segmentation}},
volume = {2019-April},
year = {2019}
@article{Zhao2019,
author = {Zhao, Yongqiang and Han, Rui and Rao, Yuan},
doi = {10.1109/ICVRIS.2019.00110},
isbn = {9781728150505},
journal = {Proceedings - 2019 International Conference on Virtual Reality and Intelligent Systems, ICVRIS 2019},
keywords = {Accurate,Fast,New Feature Pyramid},
pages = {428--431},
title = {{A new feature pyramid network for object detection}},
year = {2019}
@article{Liao2019,
archivePrefix = {arXiv},
arxivId = {1911.08947},
author = {Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
doi = {10.1609/aaai.v34i07.6812},
eprint = {1911.08947},
issn = {2159-5399},
journal = {arXiv},
title = {{Real-time scene text detection with differentiable binarization}},
year = {2019}
@article{Huang2019,
archivePrefix = {arXiv},
arxivId = {1811.09058},
author = {Huang, Zhida and Zhong, Zhuoyao and Sun, Lei and Huo, Qiang},
doi = {10.1109/WACV.2019.00086},
eprint = {1811.09058},
isbn = {9781728119755},
journal = {Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019},
pages = {764--772},
title = {{Mask R-CNN with pyramid attention network for scene text detection}},
year = {2019}
@article{Tian2019,
author = {Tian, Zhuotao and Shu, Michelle and Lyu, Pengyuan and Li, Ruiyu and Zhou, Chao and Shen, Xiaoyong and Jia, Jiaya},
doi = {10.1109/CVPR.2019.00436},
isbn = {9781728132938},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
keywords = {Categorization,Recognition: Detection,Retrieval,Vision Applications and Systems},
pages = {4229--4238},
title = {{Learning shape-aware embedding for scene text detection}},
volume = {2019-June},
year = {2019}
@article{Xu2019,
archivePrefix = {arXiv},
arxivId = {1812.01393},
author = {Xu, Yongchao and Wang, Yukang and Zhou, Wei and Wang, Yongpan and Yang, Zhibo and Bai, Xiang},
doi = {10.1109/TIP.2019.2900589},
eprint = {1812.01393},
issn = {19410042},
journal = {IEEE transactions on image processing : a publication of the IEEE Signal Processing Society},
number = {11},
pages = {5566--5579},
pmid = {30802859},
title = {{TextField: Learning a Deep Direction Field for Irregular Scene Text Detection}},
volume = {28},
year = {2019}
@article{Yang2019,
archivePrefix = {arXiv},
arxivId = {1908.01957},
author = {Yang, Mingkun and Guan, Yushuo and Liao, Minghui and He, Xin and Bian, Kaigui and Bai, Song and Yao, Cong and Bai, Xiang},
doi = {10.1109/ICCV.2019.00924},
eprint = {1908.01957},
isbn = {9781728148038},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {9146--9155},
title = {{Symmetry-constrained rectification network for scene text recognition}},
volume = {2019-Octob},
year = {2019}
@article{Lyu2019,
archivePrefix = {arXiv},
arxivId = {1906.05708},
author = {Lyu, Pengyuan and Yang, Zhicheng and Leng, Xinhang and Wu, Xiaojun and Li, Ruiyu and Shen, Xiaoyong},
eprint = {1906.05708},
journal = {arXiv},
title = {{2D attentional irregular scene text recognizer}},
year = {2019}
@article{Lu2019,
archivePrefix = {arXiv},
arxivId = {1910.02562},
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