{"id":13444374,"url":"https://github.com/whitelok/image-text-localization-recognition","last_synced_at":"2025-03-20T18:32:21.049Z","repository":{"id":49318883,"uuid":"81423389","full_name":"whitelok/image-text-localization-recognition","owner":"whitelok","description":"A general list of resources to image text localization and recognition  场景文本位置感知与识别的论文资源与实现合集 シーンテキストの位置認識と識別のための論文リソースの要約","archived":false,"fork":false,"pushed_at":"2023-09-17T13:41:49.000Z","size":341,"stargazers_count":937,"open_issues_count":0,"forks_count":237,"subscribers_count":76,"default_branch":"master","last_synced_at":"2024-08-01T04:02:07.826Z","etag":null,"topics":["awesome","convolutional-neural-networks","deep-learning","deep-learning-algorithms","machine-learning","ocr","scene-texts","text-detection","text-extraction","text-recognition"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/whitelok.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2017-02-09T07:41:41.000Z","updated_at":"2024-07-12T08:22:10.000Z","dependencies_parsed_at":"2024-01-13T22:54:54.832Z","dependency_job_id":"98275dd1-46b5-4d06-9377-400970b78e61","html_url":"https://github.com/whitelok/image-text-localization-recognition","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/whitelok%2Fimage-text-localization-recognition","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/whitelok%2Fimage-text-localization-recognition/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/whitelok%2Fimage-text-localization-recognition/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/whitelok%2Fimage-text-localization-recognition/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/whitelok","download_url":"https://codeload.github.com/whitelok/image-text-localization-recognition/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":221792892,"owners_count":16881289,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["awesome","convolutional-neural-networks","deep-learning","deep-learning-algorithms","machine-learning","ocr","scene-texts","text-detection","text-extraction","text-recognition"],"created_at":"2024-07-31T04:00:21.231Z","updated_at":"2024-10-28T06:30:42.510Z","avatar_url":"https://github.com/whitelok.png","language":null,"funding_links":[],"categories":["Uncategorized","References","Other lists","Others"],"sub_categories":["Uncategorized"],"readme":"# Scene Text Localization \u0026 Recognition Resources\n\n*Read this institute-wise: [English](README.md), [简体中文](README.zh-cn.md).*\n\n*Read this year-wise: [English](README.yearwise.md), [简体中文](README.zh-cn.yearwise.md).*\n\n*Tags: [STL] (Scene Text Localization), [TR] (Text Recognition)*\n\n*[STL] (Scene Text Localization) Detect text area from scene input image*\n\n*[TR] (Text Recognition) Recognize text content*\n\n**Last update: Sep.17 2023**\n\n## 1. Papers \u0026 Code\n\n#### Overview\n\n- [2020-arxiv] Text Detection and Recognition in the Wild: A Review [`paper`](https://arxiv.org/pdf/2006.04305.pdf)\n- [2020-arxiv] Text Recognition in the Wild: A Survey [`paper`](https://arxiv.org/pdf/2005.03492.pdf)\n- [2020-IJCV] Scene Text Detection and Recognition: The Deep Learning Era [`paper`](https://arxiv.org/pdf/1811.04256.pdf)\n- [2019-ICCV] What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis [`paper`](http://openaccess.thecvf.com/content_ICCV_2019/html/Baek_What_Is_Wrong_With_Scene_Text_Recognition_Model_Comparisons_Dataset_ICCV_2019_paper.html) [`code`](https://github.com/clovaai/deep-text-recognition-benchmark)\n- [2016-TIP] Text Detection Tracking and Recognition in Video: A Comprehensive Survey [`paper`](http://ieeexplore.ieee.org/application/enterprise/entconfirmation.jsp?arnumber=7452620\u0026icp=false)\n- [2015-PAMI] Text Detection and Recognition in Imagery: A Survey [`paper`](http://lampsrv02.umiacs.umd.edu/pubs/Papers/qixiangye-14/qixiangye-14.pdf)\n- [2014-Front.Comput.Sci] Scene Text Detection and Recognition: Recent Advances and Future Trends [`paper`](http://mc.eistar.net/uploadfiles/Papers/FCS_TextSurvey_2015.pdf)\n\n#### University of Oxford\n\n- [2020-ECCV][STL][TR] Adaptive Text Recognition through Visual Matching [`paper`](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2492_ECCV_2020_paper.php) [`code`](https://github.com/Chuhanxx/FontAdaptor)\n- [2018-BMVC][TR] Inductive Visual Localisation: Factorised Training for Superior Generalisation [`paper`](https://arxiv.org/abs/1807.08179)\n- [2016-IJCV][STL][TR] Reading Text in the Wild with Convolutional Neural Networks  [`paper`](http://arxiv.org/abs/1412.1842) [`demo`](http://zeus.robots.ox.ac.uk/textsearch/#/search/)  [`homepage`](http://www.robots.ox.ac.uk/~vgg/research/text/)\n- [2016-CVPR][STL] Synthetic Data for Text Localisation in Natural Images [`paper`](http://www.robots.ox.ac.uk/~vgg/data/scenetext/gupta16.pdf) [`code`](https://github.com/ankush-me/SynthText) [`data`](http://www.robots.ox.ac.uk/~vgg/data/scenetext/)\n- [2015-ICLR][TR] Deep structured output learning for unconstrained text recognition [`paper`](http://arxiv.org/abs/1412.5903)\n- [2015-PhD Thesis][STL] Deep Learning for Text Spotting\n [`paper`](http://www.robots.ox.ac.uk/~vgg/publications/2015/Jaderberg15b/jaderberg15b.pdf) [`code`](https://bitbucket.org/jaderberg/eccv2014_textspotting)\n- [2014-ECCV][STL] Deep Features for Text Spotting [`paper`](http://www.robots.ox.ac.uk/~vgg/publications/2014/Jaderberg14/jaderberg14.pdf) [`code`](https://bitbucket.org/jaderberg/eccv2014_textspotting) [`model`](https://bitbucket.org/jaderberg/eccv2014_textspotting)\n- [2014-NIPS][TR] Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition [`paper`](http://www.robots.ox.ac.uk/~vgg/publications/2014/Jaderberg14c/jaderberg14c.pdf)  [`homepage`](http://www.robots.ox.ac.uk/~vgg/publications/2014/Jaderberg14c/) [`model`](http://www.robots.ox.ac.uk/~vgg/research/text/model_release.tar.gz)\n\n#### Shenzhen Institutes of Advanced Technology\n\n- [2018-arxiv][STL][TR] FOTS: Fast Oriented Text Spotting with a Unified Network [`paper`](https://arxiv.org/abs/1801.01671)\n- [2016-ECCV][STL] CTPN: Detecting Text in Natural Image with Connectionist Text Proposal Network [`paper`](https://arxiv.org/abs/1609.03605) [`code`](https://github.com/tianzhi0549/CTPN)\n- [2016-CVPR][STL] Accurate Text Localization in Natural Image with Cascaded Convolutional Text Network [`paper`](http://arxiv.org/abs/1603.09423)\n- [2016-AAAI][STL] Reading Scene Text in Deep Convolutional Sequences [`paper`](http://whuang.org/papers/phe2016_aaai.pdf)\n- [2016-TIP][STL] Text-Attentional Convolutional Neural Networks for Scene Text Detection [`paper`](http://whuang.org/papers/the2016_tip.pdf)\n- [2016-TIP][STL] Text-Attentional Convolutional Neural Network for Scene Text Detection [`paper`](https://arxiv.org/pdf/1510.03283.pdf)\n- [2014-ECCV][STL] Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees [`paper`](http://www.whuang.org/papers/whuang2014_eccv.pdf)\n\n#### South China University of Technology\n\n- [2021-IJCV][STL] Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection [`paper`](https://arxiv.org/pdf/1912.09629.pdf) [`code`](https://github.com/Yuliang-Liu/Box_Discretization_Network)\n- [2021-CVPR][STL] Fourier Contour Embedding for Arbitrary-Shaped Text Detection [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Fourier_Contour_Embedding_for_Arbitrary-Shaped_Text_Detection_CVPR_2021_paper.pdf)\n- [2021-CVPR][TR][STL] Implicit Feature Alignment: Learn To Convert Text Recognizer to Text Spotter [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Implicit_Feature_Alignment_Learn_To_Convert_Text_Recognizer_to_Text_CVPR_2021_paper.pdf) [`code`](https://github.com/Wang-Tianwei/Implicit-feature-alignment)\n- [2020-CVPR][TR] Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition [`paper`](https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Learn_to_Augment_Joint_Data_Augmentation_and_Network_Optimization_for_CVPR_2020_paper.html) [`code`](https://github.com/Canjie-Luo/Text-Image-Augmentation)\n- [2020-AAAI][STL][TR] Decoupled Attention Network for Text Recognition [`paper`](https://arxiv.org/pdf/1912.10205.pdf)\n- [2020-CVPR][STL][TR] ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network [`paper`](https://arxiv.org/pdf/2002.10200.pdf) [`code`](https://github.com/Yuliang-Liu/bezier_curve_text_spotting)\n- [2020-IJCV][TR] Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild [`paper`](https://arxiv.org/pdf/2001.04189.pdf)\n- [2019-Pattern Recognition][TR] A Multi-Object Rectified Attention Network for Scene Text Recognition [`paper`](https://arxiv.org/pdf/1901.03003.pdf) [`code`](https://github.com/Canjie-Luo/MORAN_v2)\n- [2019-CVPR][TR] Aggregation Cross-Entropy for Sequence Recognition [`paper`](https://arxiv.org/pdf/1904.08364.pdf) [`code`](https://github.com/summerlvsong/Aggregation-Cross-Entropy)\n- [2019-arxiv][STL] Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection [`paper`](https://arxiv.org/pdf/1912.09629.pdf) [`code`](https://github.com/Yuliang-Liu/Box_Discretization_Network) [`code`](https://git.io/TextDet)\n- [2019-CVPR][STL] Tightness-Aware Evaluation Protocol for Scene Text Detection [`paper`](http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Tightness-Aware_Evaluation_Protocol_for_Scene_Text_Detection_CVPR_2019_paper.html)\n- [2018-AAAI][STL] Feature Enhancement Network: A Refined Scene Text Detector [`paper`](https://arxiv.org/pdf/1711.04249.pdf)\n- [2017-arXiv][STL] Detecting Curve Text in the Wild: New Dataset and New Solution [`paper`](https://arxiv.org/pdf/1712.02170)\n- [2020-arxiv][TR] Adaptive Embedding Gate for Attention-Based Scene Text Recognition [`paper`](https://arxiv.org/pdf/1908.09475.pdf)\n- [2017-PAMI][TR] Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition [`paper`](http://discovery.ucl.ac.uk/1569458/1/TPAMI-2016-08-0656-R2.pdf)\n- [2017-CVPR][STL] Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection [`paper`](https://arxiv.org/abs/1703.01425)\n- [2016-arXiv][STL] DeepText:A Unified Framework for Text Proposal Generation and Text Detection in Natural Images [`paper`](http://arxiv.org/abs/1605.07314)\n- [2016-IEEE Transactions on Multimedia][STL] A Convolutional Neural Network Based Chinese Text Detection Algorithm Via Text Structure Modeling [`paper`](http://www2.egr.uh.edu/~zhan2/ECE6111_spring2017/A%20Convolutional%20Neural%20Network%20%20Based%20Chinese%20Text%20Detection%20Algorithm%20Via%20Text%20Structure%20Modeling.pdf)\n\n#### Fudan University\n\n- [2022-AAAI][TR] Text Gestalt: Stroke-Aware Scene Text Image Super-resolution [`paper`](https://ojs.aaai.org/index.php/AAAI/article/view/19904) [`code`](https://github.com/FudanVI/FudanOCR)\n- [2023-MM][TR] Chinese Character Recognition with Augmented Character Profile Matching [`paper`](https://dl.acm.org/doi/abs/10.1145/3503161.3547827) [`code`](https://github.com/FudanVI/FudanOCR)\n- [2023-ICCV][TR] Chinese Text Recognition with A Pre-Trained CLIP-Like Model Through Image-IDS Aligning [`paper`](https://arxiv.org/abs/2309.01083) [`code`](https://github.com/FudanVI/FudanOCR)\n- [2023-arxiv][STL][TR] Weakly-Supervised Text Instance Segmentation [`paper`](https://arxiv.org/abs/2303.10848) [`code`](https://github.com/FudanVI/FudanOCR)\n- [2023-IJCAI][TR] Orientation-Independent Chinese Text Recognition in Scene Images [`paper`](https://www.ijcai.org/proceedings/2023/0185.pdf)\n- [2023-IJCAI][TR] TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition [`paper`](https://www.ijcai.org/proceedings/2023/0197.pdf) [`code`](https://github.com/simplify23/TPS_PP)\n- [2023-IJCAI][STL][TR] Towards Accurate Video Text Spotting with Text-wise Semantic Reasoning [`paper`](https://www.ijcai.org/proceedings/2023/0206.pdf) [`code`](https://github.com/FudanVI/FudanOCR)\n- [2022-MM][TR] Chinese Character Recognition with Augmented Character Profile Matching [`paper`](https://dl.acm.org/doi/abs/10.1145/3503161.3547827) [`code`](https://github.com/FudanVI/FudanOCR)\n- [2022-WACV][TR] Robustly Recognizing Irregular Scene Text by Rectifying Principle Irregularities [`paper`](https://openaccess.thecvf.com/content/WACV2022/papers/Xu_Robustly_Recognizing_Irregular_Scene_Text_by_Rectifying_Principle_Irregularities_WACV_2022_paper.pdf)\n- [2021-IJCAI][TR] Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition [`paper`](https://www.ijcai.org/proceedings/2021/0085.pdf) [`code`](https://github.com/FudanVI/FudanOCR)\n- [2022-IJCAI][TR] C3-STISR: Scene Text Image Super-resolution with Triple Clues [`paper`](https://www.ijcai.org/proceedings/2022/0238.pdf) [`code`][https://github.com/zhaominyiz/C3-STISR]\n- [2021-CVPR][TR] Scene Text Telescope: Text-Focused Scene Image Super-Resolution [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Scene_Text_Telescope_Text-Focused_Scene_Image_Super-Resolution_CVPR_2021_paper.pdf)\n- [2020-arxiv][TR] Text Recognition in Real Scenarios with a Few Labeled Samples [`paper`](https://arxiv.org/pdf/2006.12209.pdf)\n- [2018-CVPR][TR] Edit Probability for Scene Text Recognition [`paper`](http://openaccess.thecvf.com/content_cvpr_2018/papers/Bai_Edit_Probability_for_CVPR_2018_paper.pdf)\n- [2017-arXiv][STL] Arbitrary-Oriented Scene Text Detection via Rotation Proposals [`paper`](https://arxiv.org/abs/1703.01086) [`code`](https://github.com/mjq11302010044/RRPN)\n\n#### Huazhong University of Science and Technology\n\n- [2021-CVPR][STL][TR] Scene Text Retrieval via Joint Text Detection and Similarity Learning [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Scene_Text_Retrieval_via_Joint_Text_Detection_and_Similarity_Learning_CVPR_2021_paper.pdf) [`code`](https://github.com/lanfeng4659/STR-TDSL)\n- [2021-CVPR][STL] MOST: A Multi-Oriented Scene Text Detector With Localization Refinement [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/He_MOST_A_Multi-Oriented_Scene_Text_Detector_With_Localization_Refinement_CVPR_2021_paper.pdf)\n- [2020-ECCV][TR] AutoSTR: Efficient Backbone Search for Scene Text Recognition [`paper`](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4796_ECCV_2020_paper.php)\n- [2020-AAAI][STL][TR] All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting [`paper`](https://arxiv.org/pdf/1911.09550.pdf)\n- [2020-AAAI][STL] Real-time Scene Text Detection with Differentiable Binarization [`paper`](https://arxiv.org/pdf/1911.08947.pdf) [`code`](https://github.com/MhLiao/DB)\n- [2020-ECCV][STL][TR] Mask TextSpotter V3: Segmentation Proposal Network for Robust Scene Text Spotting [`paper`](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1436_ECCV_2020_paper.php) [`code`](https://github.com/MhLiao/MaskTextSpotterV3)\n- [2019-PAMI][TR] ASTER: An Attentional Scene Text Recognizer with Flexible Rectification [`paper`](https://ieeexplore.ieee.org/document/8395027) [`code`](https://github.com/ayumiymk/aster.pytorch)\n- [2019-AAAI][TR] Scene Text Recognition from Two-Dimensional Perspective [`paper`](https://arxiv.org/pdf/1809.06508.pdf)\n- [2019-PAMI][STL] Gliding vertex on the horizontal bounding box for multi-oriented object detection [`paper`](https://arxiv.org/pdf/1911.09358.pdf) [`code`](https://github.com/MingtaoFu/gliding_vertex)\n- [2019-ICCV][TR] Symmetry-Constrained Rectification Network for Scene Text Recognition [`paper`](http://openaccess.thecvf.com/content_ICCV_2019/html/Yang_Symmetry-Constrained_Rectification_Network_for_Scene_Text_Recognition_ICCV_2019_paper.html)\n- [2018-arxiv][STL] TextField: Learning A Deep Direction Field for Irregular Scene Text Detection [`paper`](https://arxiv.org/pdf/1812.01393.pdf) [`code`](https://github.com/YukangWang/TextField)\n- [2018-ECCV][TR][STL] Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes [`paper`](http://openaccess.thecvf.com/content_ECCV_2018/papers/Pengyuan_Lyu_Mask_TextSpotter_An_ECCV_2018_paper.pdf)\n- [2018-ICIP][STL] Feature Fusion Network for Scene Text Detection [`paper`](https://ieeexplore.ieee.org/document/8395194/)\n- [2018-CVPR][STL] Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation [`paper`](http://openaccess.thecvf.com/content_cvpr_2018/papers/Lyu_Multi-Oriented_Scene_Text_CVPR_2018_paper.pdf)\n- [2018-CVPR][STL] Rotation-sensitive Regression for Oriented Scene Text Detection [`paper`](http://openaccess.thecvf.com/content_cvpr_2018/papers/Liao_Rotation-Sensitive_Regression_for_CVPR_2018_paper.pdf)\n- [2018-TIP][STL] TextBoxes++: A Single-Shot Oriented Scene Text Detector [`paper`](https://arxiv.org/abs/1801.02765) [`code`](https://github.com/MhLiao/TextBoxes_plusplus)\n- [2017-AAAI][STL] TextBoxes: A Fast TextDetector with a Single Deep Neural Network [`paper`](https://arxiv.org/abs/1611.06779) [`code`](https://github.com/MhLiao/TextBoxes)\n- [2017-CVPR][STL] Detecting Oriented Text in Natural Images by Linking Segments [`paper`](http://mclab.eic.hust.edu.cn/UpLoadFiles/Papers/SegLink_CVPR17.pdf) [`code`](https://github.com/bgshih/seglink)\n- [2016-CVPR][TR] Robust scene text recognition with automatic rectification [`paper`](http://arxiv.org/pdf/1603.03915v2.pdf)\n- [2016-arXiv][STL] Scene Text Detection via Holistic, Multi-Channel Prediction [`paper`](https://arxiv.org/abs/1606.09002)\n- [2016-CVPR][STL] Multi-oriented text detection with fully convolutional networks    [`paper`](http://mclab.eic.hust.edu.cn/UpLoadFiles/Papers/TextDectionFCN_CVPR16.pdf)\n- [2015-PAMI][TR] An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition [`paper`](http://arxiv.org/pdf/1507.05717v1.pdf) [`code`](http://mclab.eic.hust.edu.cn/~xbai/CRNN/crnn_code.zip) [`code`](https://github.com/bgshih/crnn)\n- [2014-CVPR][TR] Strokelets: A Learned Multi-Scale Representation for Scene Text Recognition [`paper`](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Yao_Strokelets_A_Learned_2014_CVPR_paper.pdf)\n\n#### Universitat Autònoma de Barcelona\n\n- [2019-ICCV][STL][TR] Scene Text Visual Question Answering [`paper`](http://openaccess.thecvf.com/content_ICCV_2019/html/Biten_Scene_Text_Visual_Question_Answering_ICCV_2019_paper.html)\n- [2018-ECCV][STL] Single Shot Scene Text Retrieval [`paper`](http://openaccess.thecvf.com/content_ECCV_2018/papers/Lluis_Gomez_Single_Shot_Scene_ECCV_2018_paper.pdf)\n- [2017-arXiv][STL] Improving Text Proposal for Scene Images with Fully Convolutional Networks [`paper`](https://arxiv.org/abs/1702.05089)\n- [2016-arXiv][STL] TextProposals: a Text-specific Selective Search Algorithm for Word Spotting in the Wild [`paper`](https://arxiv.org/pdf/1604.02619.pdf) [`code`](https://github.com/lluisgomez/TextProposals)\n- [2015-ICDAR][STL] Object Proposals for Text Extraction in the Wild [`paper`](http://arxiv.org/abs/1509.02317) [`code`](https://github.com/lluisgomez/TextProposals)\n- [2014-PAMI][TR] Word Spotting and Recognition with Embedded Attributes [`paper`](http://www.cvc.uab.es/~afornes/publi/journals/2014_PAMI_Almazan.pdf) [`homepage`](http://www.cvc.uab.es/~almazan/index/projects/words-att/index.html) [`code`](https://github.com/almazan/watts)\n\n#### Stanford University\n\n- [2012-ICPR][TR] End-to-End Text Recognition with Convolutional Neural Networks [`paper`](http://www.cs.stanford.edu/~acoates/papers/wangwucoatesng_icpr2012.pdf) [`code`](http://cs.stanford.edu/people/twangcat/ICPR2012_code/SceneTextCNN_demo.tar) [`SVHN Dataset`](http://ufldl.stanford.edu/housenumbers/)\n- [2012-PhD Thesis][TR] End-to-End Text Recognition with Convolutional Neural Networks [`paper`](http://cs.stanford.edu/people/dwu4/HonorThesis.pdf)\n\n#### Seoul National University\n\n- [2017-AAAI][STL][TR] Detection and Recognition of Text Embedding in Online Images via Neural Context Models [`paper`](https://github.com/cmkang/CTSN/blob/master/aaai2017_cameraready.pdf)\n\n#### Megvii Technology Inc: Face++\n\n- [2020-CVPR][TR] On Vocabulary Reliance in Scene Text Recognition [`paper`](https://openaccess.thecvf.com/content_CVPR_2020/html/Wan_On_Vocabulary_Reliance_in_Scene_Text_Recognition_CVPR_2020_paper.html)\n- [2020-AAAI][STL][TR] TextScanner: Reading Characters in Order for Robust Scene Text Recognition [`paper`](https://arxiv.org/pdf/1912.12422.pdf)\n- [2017-CVPR][STL] EAST: An Efficient and Accurate Scene Text Detector [`paper`](https://arxiv.org/abs/1704.03155) [`code`](https://github.com/argman/EAST) [`code with improvement`](https://github.com/huoyijie/AdvancedEAST)\n\n#### Institute of Automation, Chinese Academy of Sciences\n\n- [2020-IJCV][STL][TR] Residual Dual Scale Scene Text Spotting by Fusing Bottom-Up and Top-Down Processing [`paper`](https://link.springer.com/article/10.1007/s11263-020-01388-x)\n- [2019-CVPR][TR] Sequence-to-Sequence Domain Adaptation Networkfor Robust Text Image Recognition [`paper`](https://ieeexplore.ieee.org/abstract/document/8953495)\n- [2019-ICCV][STL][TR] TextDragon: An End-to-End Framework for Arbitrary Shaped Text Spotting [`paper`](http://openaccess.thecvf.com/content_ICCV_2019/html/Feng_TextDragon_An_End-to-End_Framework_for_Arbitrary_Shaped_Text_Spotting_ICCV_2019_paper.html)\n- [2018-arxiv][TR] NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition [`paper`](https://arxiv.org/pdf/1806.00926.pdf) [`code`](https://github.com/Belval/NRTR)\n- [2018-arxiv][TR] SCAN: Sliding Convolutional Attention Network for Scene Text Recognition [`paper`](https://arxiv.org/pdf/1806.00578.pdf) [`code`](https://github.com/nameful/SCAN)\n- [2018-arxiv][TR] Recurrent Calibration Network for Irregular Text Recognition [`paper`](https://arxiv.org/pdf/1812.07145.pdf)  \n- [2017-arxiv][TR] Scene Text Recognition with Sliding Convolutional Character Models [`paper`](https://arxiv.org/pdf/1709.01727.pdf) [`code`](https://github.com/lsvih/Sliding-Convolution)\n- [2017-arXiv][STL] Deep Direct Regression for Multi-Oriented Scene Text Detection [`paper`](https://arxiv.org/abs/1703.08289)\n- [2017-IAPR][STL] Scene Text Detection with Novel Superpixel Based Character Candidate Extraction [`paper`](https://ieeexplore.ieee.org/abstract/document/8270087)\n\n#### University of California, San Diego\n\n- [2016-CVPR][TR] Recursive Recurrent Nets with Attention Modeling for OCR in the Wild [`paper`](http://arxiv.org/pdf/1603.03101v1.pdf)\n\n#### University of California, Santa Cruz\n\n- [2017-arXiv][STL] Cascaded Segmentation-Detection Networks for Word-Level Text Spotting [`paper`](https://arxiv.org/abs/1704.00834)\n\n#### Cornell University\n\n- [2016-arXiv][STL][TR] COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images [`paper`](http://vision.cornell.edu/se3/wp-content/uploads/2016/01/1601.07140v1.pdf)\n\n#### Pennsylvania State University\n\n- [2017-WACV][STL] TextContourNet: A Flexible and Effective Framework for Improving Scene Text Detection Architecture With a Multi-Task Cascade [`paper`](https://arxiv.org/pdf/1809.03050.pdf)\n- [2016-PhD Thesis][STL] Context Modeling for Semantic Text Matching and Scene Text Detection [`paper`](https://etda.libraries.psu.edu/catalog/zw12z528p)\n\n#### University of Science and Technology Beijing\n\n- [2021-ICCV][STL] Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection [`paper`](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Adaptive_Boundary_Proposal_Network_for_Arbitrary_Shape_Text_Detection_ICCV_2021_paper.pdf) [`code`](https://github.com/GXYM/TextBPN)\n- [2020-CVPR][STL] Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection [`paper`](https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Deep_Relational_Reasoning_Graph_Network_for_Arbitrary_Shape_Text_Detection_CVPR_2020_paper.html)\n- [2017-arxiv][TR] AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition [`paper`](https://arxiv.org/pdf/1710.03425.pdf)\n- [2016-IJCAI][STL] Scene Text Detection in Video by Learning Locally and Globally [`paper`](https://www.ijcai.org/Proceedings/16/Papers/376.pdf)\n- [2014-PAMI][TR] Robust Text Detection in Natural Scene Images [`paper`](http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6613482)\n\n#### Pohang University of Science and Technology\n\n- [2016-CVPR][STL] CannyText Detector: Fast and Robust Scene Text Localization Algorithm [`paper`](http://ieeexplore.ieee.org/document/7780757/)\n\n#### École d'Ingénieurs en Informatique\n\n- [2016-IJDAR][STL] TextCatcher: a method to detect curved and challenging text in natural scenes [`paper`](https://link.springer.com/article/10.1007/s10032-016-0264-4)\n\n#### České vysoké učení technické v Praze. Czech Technical University\n\n- [2018-ACCV][STL][TR] E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text [`paper`](https://arxiv.org/pdf/1801.09919.pdf) [`code`](https://github.com/MichalBusta/E2E-MLT)\n- [2017-ICCV][STL][TR] Deep TextSpotter: An End-to-End Trainable Scene Text Localization and\nRecognition Framework [`peper`](http://openaccess.thecvf.com/content_ICCV_2017/papers/Busta_Deep_TextSpotter_An_ICCV_2017_paper.pdf) [`code`](https://github.com/MichalBusta/DeepTextSpotter)\n- [2015-PAMI][STL][TR] Real-time Lexicon-free Scene Text Localization and Recognition [`paper`](http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7313008)\n- [2015-ICCV][STL] FASText: Efficient unconstrained scene text detector [`paper`](https://pdfs.semanticscholar.org/2131/106318d4674bc9260e671c9f427bfc3f1029.pdf) [`code`](https://github.com/MichalBusta/FASText)\n- [2012-CVPR][STL][TR] Real-time scene text localization and recognition [`paper`](http://cmp.felk.cvut.cz/~matas/papers/neumann-2012-rt_text-cvpr.pdf) [`code`](http://docs.opencv.org/3.0-beta/modules/text/doc/erfilter.html)\n\n#### Google Inc\n\n- [2019-ICCV][STL] Towards Unconstrained End-to-End Text Spotting [`paper`](http://openaccess.thecvf.com/content_ICCV_2019/html/Qin_Towards_Unconstrained_End-to-End_Text_Spotting_ICCV_2019_paper.html)\n- [2013-ICCV][STL][TR] Photo OCR: Reading Text in Uncontrolled Conditions [`paper`](https://ai2-s2-pdfs.s3.amazonaws.com/31a8/803d7e2618bfa44c472d003055bb5961b9de.pdf)\n\n#### Microsoft Inc\n\n- [2010-CVPR][STL] SWT: Detecting Text in Natural Scenes with Stroke Width Transform [`paper`](http://www.math.tau.ac.il/~turkel/imagepapers/text_detection.pdf) [`code`](https://github.com/aperrau/DetectText)\n\n#### Samsung R\u0026D Institute China\n\n- [2019-CVPR][STL] Arbitrary Shape Scene Text Detection With Adaptive Text Region Representation [`paper`](http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Arbitrary_Shape_Scene_Text_Detection_With_Adaptive_Text_Region_Representation_CVPR_2019_paper.html)\n- [2017-arXiv][STL] R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection [`paper`](https://arxiv.org/ftp/arxiv/papers/1706/1706.09579.pdf)\n- [2017-IAPR][STL] Deep Residual Text Detection Network for Scene Text [`paper`](https://ieeexplore.ieee.org/document/8270068)\n\n#### Vicarious FPC Inc\n\n- [2016-NIPS][TR] Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data [`paper`](https://arxiv.org/abs/1611.02788)\n\n#### Chinese State Key Laboratory of Management and Control for Complex Systems\n\n- [2013-CVPR][TR] Scene Text Recognition using Part-based Tree-structured Character Detection [`paper`](http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Shi_Scene_Text_Recognition_2013_CVPR_paper.pdf)\n\n#### Stanford University\n\n- [2012-ICPR][TR] End-to-End Text Recognition with CNN [`paper`](http://www.cs.stanford.edu/~acoates/papers/wangwucoatesng_icpr2012.pdf) [`code`](http://cs.stanford.edu/people/twangcat/ICPR2012_code/SceneTextCNN_demo.tar)\n\n#### Visual Computing Department, Institute for Infocomm Research\n\n- [2017-ICCV][STL] WeText: Scene Text Detection under Weak Supervision [`paper`](http://openaccess.thecvf.com/content_ICCV_2017/papers/Tian_WeText_Scene_Text_ICCV_2017_paper.pdf)\n\n#### University of Florida\n\n- [2017-ICCV][STL] Single Shot Text Detector with Regional Attention [`paper`](http://openaccess.thecvf.com/content_ICCV_2017/papers/He_Single_Shot_Text_ICCV_2017_paper.pdf) [`code`](https://github.com/BestSonny/SSTD)\n\n#### University of Southern California\n\n- [2017-ICCV][STL] Self-organized Text Detection with Minimal Post-processing via Border Learning [`paper`](http://openaccess.thecvf.com/content_ICCV_2017/papers/Wu_Self-Organized_Text_Detection_ICCV_2017_paper.pdf)\n\n#### Hikvision Research Institute\n\n- [2021-AAAI][STL][TR] MANGO: A Mask Attention Guided One-Stage Scene Text Spotter [`paper`](https://arxiv.org/pdf/2012.04350.pdf)\n- [2020-AAAI][STL][TR] Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting [`paper`](https://arxiv.org/pdf/2002.06820.pdf)\n- [2018-CVPR][TR] AON: Towards Arbitrarily-Oriented Text Recognition [`paper`](https://arxiv.org/pdf/1711.04226.pdf) [`code`](https://github.com/huizhang0110/AON)\n- [2017-ICCV][TR] Focusing Attention: Towards Accurate Text Recognition in Natural Images [`paper`](http://openaccess.thecvf.com/content_ICCV_2017/papers/Cheng_Focusing_Attention_Towards_ICCV_2017_paper.pdf)\n\n#### University of Adelaide\n\n- [2019-AAAI][TR] Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition [`paper`](https://arxiv.org/pdf/1811.00751.pdf) [`code`](https://github.com/Pay20Y/SAR_TF)\n- [2017-ICCV][STL][TR] Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks [`paper`](http://openaccess.thecvf.com/content_ICCV_2017/papers/Li_Towards_End-To-End_Text_ICCV_2017_paper.pdf)\n\n#### City University of New York\n\n- [2017-CVPR][STL] Unambiguous Text Localization and Retrieval for Cluttered Scenes [`paper`](http://openaccess.thecvf.com/content_cvpr_2017/papers/Rong_Unambiguous_Text_Localization_CVPR_2017_paper.pdf)\n\n#### The University of Hong Kong\n\n- [2020-ECCV][STL][TR] AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting [`paper`](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2183_ECCV_2020_paper.php)\n- [2018-AAAI][TR] Char-Net: A Character-Aware Neural Network for Distorted Scene Text [`paper`](http://www.visionlab.cs.hku.hk/publications/wliu_aaai18.pdf)\n\n#### Zhejiang University\n\n- [2021-TIP][STL][TR] FREE: A Fast and Robust End-to-End Video Text Spotter [`paper`](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9266586)\n- [2020-arxiv][TR] Refined Gate: A Simple and Effective Gating Mechanism for Recurrent Units [`paper`](https://arxiv.org/pdf/2002.11338.pdf)\n- [2018-AAAI][STL] PixelLink: Detecting Scene Text via Instance Segmentation [`paper`](https://arxiv.org/pdf/1801.01315.pdf)\n\n#### University of Potsdam\n\n- [2018-AAAI][STL][TR] SEE: Towards Semi-Supervised End-to-End Scene Text Recognition [`paper`](https://arxiv.org/pdf/1712.05404.pdf) [`code`](https://github.com/Bartzi/see)\n\n#### Arizona State Unviversity\n\n- [2018-AAAI][TR] SqueezedText: A Real-time Scene Text Recognition by Binary Convolutional\nEncoder-decoder Network [`paper`](https://pdfs.semanticscholar.org/9061/47e6eb8e963d9751dda18fb540ed7faeb9fb.pdf)\n\n#### Stevens Institute of Technology\n\n- [2018-CVPR][STL] Geometry-Aware Scene Text Detection with Instance Transformation Network [`paper`](http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Geometry-Aware_Scene_Text_CVPR_2018_paper.pdf)\n\n#### Nanyang Technological University\n\n- [2020-IJCV][STL] Bottom-Up Scene Text Detection with Markov Clustering Networks [`paper`](https://link.springer.com/article/10.1007/s11263-020-01298-y)\n- [2020-AAAI][STL][TR] GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognition [`paper`](https://arxiv.org/pdf/2002.01276.pdf)\n- [2019-ICCV][STL][TR] GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition [`paper`](http://openaccess.thecvf.com/content_ICCV_2019/html/Zhan_GA-DAN_Geometry-Aware_Domain_Adaptation_Network_for_Scene_Text_Detection_and_ICCV_2019_paper.html)\n- [2019-CVPR][STL] ESIR: End-To-End Scene Text Recognition via Iterative Image Rectification [`paper`](http://openaccess.thecvf.com/content_CVPR_2019/html/Zhan_ESIR_End-To-End_Scene_Text_Recognition_via_Iterative_Image_Rectification_CVPR_2019_paper.html)\n- [2019-CVPR][STL] Towards Robust Curve Text Detection With Conditional Spatial Expansion [`paper`](http://openaccess.thecvf.com/content_CVPR_2019/html/)Liu_Towards_Robust_Curve_Text_Detection_With_Conditional_Spatial_Expansion_CVPR_2019_paper.html)\n- [2018-ECCV][STL] Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes [`paper`](http://openaccess.thecvf.com/content_ECCV_2018/papers/Fangneng_Zhan_Verisimilar_Image_Synthesis_ECCV_2018_paper.pdf)\n- [2018-ECCV][STL] Accurate Scene Text Detection through Border Semantics Awareness and Bootstrapping [`paper`](http://openaccess.thecvf.com/content_ECCV_2018/papers/Chuhui_Xue_Accurate_Scene_Text_ECCV_2018_paper.pdf)\n- [2018-ECCV][STL] Using Object Information for Spotting Text [`paper`](http://openaccess.thecvf.com/content_ECCV_2018/papers/Shitala_Prasad_Using_Object_Information_ECCV_2018_paper.pdf)\n- [2018-CVPR][STL] Learning Markov Clustering Networks for Scene Text Detection [`paper`](http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Learning_Markov_Clustering_CVPR_2018_paper.pdf)\n\n#### Alibaba Group \n\n- [2018-ICPR][STL][TR] A Novel Integrated Framework for Learning both Text Detection and Recognition [`paper`](https://arxiv.org/pdf/1811.08611.pdf)\n- [2018-IJCAI][STL] IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection [`paper`](https://arxiv.org/pdf/1805.01167.pdf)\n\n#### Chinese Academy of Sciences\n\n- [2020-CVPR][STL][TR] Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text [`paper`](https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Multi-Modal_Graph_Neural_Network_for_Joint_Reasoning_on_Vision_and_CVPR_2020_paper.html)\n- [2018-ICIP][STL] Focal Text: An Accurate Text Detection With Focal Loss [`paper`](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8451241)\n- [2018-ICIP][STL] Dense Chained Attention Network for Scene Text Recognition [`paper`](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8451273)\n\n#### University of Cambridge\n\n- [2018-ECCV][STL] Synthetically Supervised Feature Learning for Scene Text Recognition [`paper`](http://openaccess.thecvf.com/content_ECCV_2018/papers/Yang_Liu_Synthetically_Supervised_Feature_ECCV_2018_paper.pdf)\n\n#### Peking University\n\n- [2021-NIPS][TR] CentripetalText: An Efficient Text Instance Representation for Scene Text Detection [`paper`](https://arxiv.org/pdf/2107.05945.pdf) [`code`](https://github.com/shengtao96/CentripetalText)\n- [2020-ICASSP][TR] A New Perspective for Flexible Feature Gathering in Scene Text Recognition Via Character Anchor Pooling [`paper`](https://arxiv.org/pdf/2002.03509.pdf)\n- [2020-ICASSP][STL] All you need is a second look: Towards Tighter Arbitrary shape text detection [`paper`](https://arxiv.org/pdf/2004.12436.pdf)\n- [2019-WACV][STL] Mask R-CNN with Pyramid Attention Network for Scene Text Detection [`paper`](https://arxiv.org/pdf/1811.09058.pdf)\n- [2018-ECCV][STL] TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes [`paper`](https://arxiv.org/pdf/1807.01544.pdf) [`code`](https://github.com/princewang1994/TextSnake.pytorch)\n\n#### SenseTime Research\n\n- [2021-WACV][STL] Disentangled Contour Learning for Quadrilateral Text Detection [`paper`](https://openaccess.thecvf.com/content/WACV2021/papers/Bi_Disentangled_Contour_Learning_for_Quadrilateral_Text_Detection_WACV_2021_paper.pdf) [`code`](https://github.com/SakuraRiven/DCLNet)\n- [2020-ECCV][TR] RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition [`paper`](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3160_ECCV_2020_paper.php)\n- [2020-ECCV][TR] Scene Text Image Super-resolution in the wild [`paper`](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1186_ECCV_2020_paper.php)\n- [2019-arxiv][STL] Pyramid Mask Text Detector [`paper`](https://arxiv.org/pdf/1903.11800.pdf)\n- [2019-ICCV][STL] Geometry Normalization Networks for Accurate Scene Text Detection [`paper`](http://openaccess.thecvf.com/content_ICCV_2019/html/Xu_Geometry_Normalization_Networks_for_Accurate_Scene_Text_Detection_ICCV_2019_paper.html)\n- [2018-BMVC][STL] Boosting up Scene Text Detectors with Guided CNN [`paper`](http://bmvc2018.org/contents/papers/0633.pdf)\n\n#### Naver Clova AI Research\n\n- [2020-ECCV][STL] Character Region Attention For Text Spotting [`paper`](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6775_ECCV_2020_paper.php)\n- [2019-CVPR][STL][TR] Character Region Awareness for Text Detection [`paper`](https://arxiv.org/abs/1904.01941) [`code`](https://github.com/clovaai/CRAFT-pytorch)\n\n#### Baidu\n\n- [2020-arxiv][STL][TR] PP-OCR: A Practical Ultra Lightweight OCR System [`paper`](https://arxiv.org/pdf/2009.09941.pdf)\n- [2019-ICCV][STL][TR] Chinese Street View Text: Large-Scale Chinese Text Reading With Partially Supervised Learning [`paper`](http://openaccess.thecvf.com/content_ICCV_2019/html/Sun_Chinese_Street_View_Text_Large-Scale_Chinese_Text_Reading_With_Partially_ICCV_2019_paper.html)\n- [2019-CVPR][STL] Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes\n[`paper`](https://arxiv.org/abs/1904.06535)\n- [2018-arxiv][STL] Detecting Text in the Wild with Deep Character Embedding Network [`paper`](https://arxiv.org/abs/1801.01671)\n- [2018-ACCV][STL][TR] TextNet: Irregular Text Reading from Images with an End-to-End Trainable Network [`paper`](https://arxiv.org/pdf/1812.09900.pdf)\n\n#### University of Adelaide \n\n- [2018-CVPR][STL][TR] An End-to-End TextSpotter with Explicit Alignment and Attention [`paper`](http://openaccess.thecvf.com/content_cvpr_2018/papers/He_An_End-to-End_TextSpotter_CVPR_2018_paper.pdf) [`code`](https://github.com/tonghe90/textspotter)\n\n#### Nanjing University\n\n- [2020-BMVC][TR] Robust Scene Text Recognition Through Adaptive Image Enhancement [`paper`](https://www.bmvc2020-conference.com/assets/papers/0257.pdf)\n- [2019-ICCV][STL] Efficient and Accurate Arbitrary-Shaped Text Detection With Pixel Aggregation Network [`paper`](http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Efficient_and_Accurate_Arbitrary-Shaped_Text_Detection_With_Pixel_Aggregation_Network_ICCV_2019_paper.html) [`code`](https://github.com/WenmuZhou/PAN.pytorch)\n- [2019-CVPR][STL] Shape Robust Text Detection With Progressive Scale Expansion Network [`paper`](http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Shape_Robust_Text_Detection_With_Progressive_Scale_Expansion_Network_CVPR_2019_paper.html) [`code`](https://github.com/whai362/PSENet)\n\n#### The Chinese University of Hong Kong\n\n- [2022-AAAI][TR] Context-based Contrastive Learning for Scene Text Recognition [`paper`](https://www.cse.cuhk.edu.hk/~byu/papers/C139-AAAI2022-ConCLR.pdf)\n- [2019-CVPR][STL] Learning Shape-Aware Embedding for Scene Text Detection [`paper`](http://openaccess.thecvf.com/content_CVPR_2019/html/Tian_Learning_Shape-Aware_Embedding_for_Scene_Text_Detection_CVPR_2019_paper.html)\n\n#### Malong Technologies\n\n- [2019-ICCV][STL][TR] Convolutional Character Networks [`paper`](http://openaccess.thecvf.com/content_ICCV_2019/html/Xing_Convolutional_Character_Networks_ICCV_2019_paper.html) [`code`](https://github.com/MalongTech/research-charnet)\n\n#### University of Rochester\n\n- [2019-ICCV][TR] Large-Scale Tag-Based Font Retrieval With Generative Feature Learning [`paper`](http://openaccess.thecvf.com/content_ICCV_2019/html/Chen_Large-Scale_Tag-Based_Font_Retrieval_With_Generative_Feature_Learning_ICCV_2019_paper.html)\n\n#### Facebook AI Research\n\n- [2021-CVPR][STL][TR] TextOCR: Towards Large-Scale End-to-End Reasoning for Arbitrary-Shaped Scene Text [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Singh_TextOCR_Towards_Large-Scale_End-to-End_Reasoning_for_Arbitrary-Shaped_Scene_Text_CVPR_2021_paper.pdf) [`code`](https://textvqa.org/textocr/code)\n- [2020-CVPR][STL][TR] Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA [`paper`](https://arxiv.org/pdf/1911.06258.pdf)\n- [2018-arxiv][STL] Improving Rotated Text Detection with Rotation Region Proposal Networks [`paper`](https://arxiv.org/pdf/1811.07031.pdf) \n\n#### University of Marlyand\n\n- [2020-WACV][TR] Adapting Style and Content for Attended Text Sequence Recognition [`paper`](http://openaccess.thecvf.com/content_WACV_2020/papers/Schwarcz_Adapting_Style_and_Content_for_Attended_Text_Sequence_Recognition_WACV_2020_paper.pdf)\n\n#### Penta-AI\n\n- [2020-WACV][STL] It’s All About The Scale - Efficient Text Detection Using Adaptive Scaling [`paper`](http://openaccess.thecvf.com/content_WACV_2020/papers/Richardson_Its_All_About_The_Scale_-_Efficient_Text_Detection_Using_WACV_2020_paper.pdf)\n\n#### Central China Normal University\n\n- [2020-ECCV][STL][TR] PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit [`paper`](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2318_ECCV_2020_paper.php)\n\n#### Tencent\n\n- [2022-AAAI][TR] Perceiving Stroke-Semantic Context: Hierarchical Contrastive Learning for Robust Scene Text Recognition [`paper`](https://www.aaai.org/AAAI22Papers/AAAI-785.LiuH.pdf)\n- [2020-arxiv][STL] PuzzleNet: Scene Text Detection by Segment Context Graph Learning [`paper`](https://arxiv.org/pdf/2002.11371.pdf)\n- [2020-AAAI][STL][TR] Accurate Structured-Text Spotting for Arithmetical Exercise Correction [`paper`](https://www.researchgate.net/publication/341891992_Accurate_Structured-Text_Spotting_for_Arithmetical_Exercise_Correction)\n- [2019-arxiv][TR] 2D Attentional Irregular Scene Text Recognizer [`paper`](https://arxiv.org/pdf/1906.05708.pdf) [`code`](https://github.com/chenjun2hao/Bert_OCR.pytorch)\n\n#### Tsinghua University\n\n- [2023-IJCAI][TR] Towards Robust Scene Text Image Super-resolution via Explicit Location Enhancement [`paper`](https://www.ijcai.org/proceedings/2023/0087.pdf) [`code`](https://github.com/csguoh/LEMMA)\n- [2021-CVPR][STL] Primitive Representation Learning for Scene Text Recognition [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Primitive_Representation_Learning_for_Scene_Text_Recognition_CVPR_2021_paper.pdf)\n- [2020-ECCV][STL] Sequential Deformation for Accurate Scene Text Detection [`paper`](http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6576_ECCV_2020_paper.php)\n\n#### University of Science and Technology of China\n\n- [2023-IJCAI][TR] Linguistic More: Taking a Further Step toward Effcient and Accurate Scene Text Recognition [`paper`](https://www.ijcai.org/proceedings/2023/0189.pdf) [`code`](https://github.com/CyrilSterling/LPV)\n- [2021-ICCV][TR] From Two to One: A New Scene Text Recognizer With Visual Language Modeling Network [`paper`](https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_From_Two_to_One_A_New_Scene_Text_Recognizer_With_ICCV_2021_paper.pdf)\n- [2021-CVPR][STL] Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Fang_Read_Like_Humans_Autonomous_Bidirectional_and_Iterative_Language_Modeling_for_CVPR_2021_paper.pdf) [`code`](https://github.com/FangShancheng/ABINet)\n- [2020-CVPR][STL] ContourNet: Taking a Further Step Toward Accurate Arbitrary-Shaped Scene Text Detection [`paper`](https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_ContourNet_Taking_a_Further_Step_Toward_Accurate_Arbitrary-Shaped_Scene_Text_CVPR_2020_paper.html) [`code`](https://github.com/wangyuxin87/ContourNet)\n- [2020-arxiv][TR] Focus-Enhanced Scene Text Recognition with Deformable Convolutions [`paper`](https://arxiv.org/pdf/1908.10998.pdf) [`code`](https://github.com/Alpaca07/dtr)\n- [2018-Pattern Recognition][STL] TextMountain: Accurate Scene Text Detection via Instance Segmentation [`paper`](https://arxiv.org/pdf/1811.12786.pdf)\n\n#### University of Electronic Science and Technology of China\n\n- [2020-CVPR][TR] What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images [`paper`](https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_What_Machines_See_Is_Not_What_They_Get_Fooling_Scene_CVPR_2020_paper.html)\n\n#### Indian Statistical Institute\n\n- [2020-CVPR][STL][TR] STEFANN: Scene Text Editor Using Font Adaptive Neural Network [`paper`](https://openaccess.thecvf.com/content_CVPR_2020/html/Roy_STEFANN_Scene_Text_Editor_Using_Font_Adaptive_Neural_Network_CVPR_2020_paper.html)\n\n#### Institute of Information Engineering, Chinese Academy of Sciences\n\n- [2021-CVPR][STL] Progressive Contour Regression for Arbitrary-Shape Scene Text Detection [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_Progressive_Contour_Regression_for_Arbitrary-Shape_Scene_Text_Detection_CVPR_2021_paper.pdf) [`code`](https://github.com/dpengwen/PCR)\n- [2020-CVPR][TR] SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition [`paper`](https://openaccess.thecvf.com/content_CVPR_2020/html/Qiao_SEED_Semantics_Enhanced_Encoder-Decoder_Framework_for_Scene_Text_Recognition_CVPR_2020_paper.html)\n- [2020-ICPR][TR] Gaussian Constrained Attention Network for Scene Text Recognition [`paper`](https://arxiv.org/pdf/2010.09169.pdf)\n- [2020-arxiv][STL] Self-Training for Domain Adaptive Scene Text Detection [`paper`](https://arxiv.org/pdf/2005.11487.pdf)\n- [2019-ICDAR][STL] Curved Text Detection in Natural Scene Images with Semi- and Weakly-Supervised Learning [`paper`](https://arxiv.org/pdf/1908.09990.pdf)\n- [2019-BMVC][TR] Text Recognition using local correlation[`paper`](https://bmvc2019.org/wp-content/uploads/papers/0469-paper.pdf)\n\n#### University of Chinese Academy of Sciences\n\n- [2020-CVPR][STL][TR] Towards Accurate Scene Text Recognition With Semantic Reasoning Networks [`paper`](https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Towards_Accurate_Scene_Text_Recognition_With_Semantic_Reasoning_Networks_CVPR_2020_paper.html)\n\n#### Amazon\n\n- [2020-CVPR][STL] SCATTER: Selective Context Attentional Scene Text Recognizer [`paper`](https://openaccess.thecvf.com/content_CVPR_2020/html/Litman_SCATTER_Selective_Context_Attentional_Scene_Text_Recognizer_CVPR_2020_paper.html)\n\n#### Heritage Institute of Technology\n\n- [2020-ICIP][STL] Scale-invariant Multi-oriented Text Detection in Wild Scene Images [`paper`](https://arxiv.org/pdf/2002.06423.pdf)\n\n#### Indian Institute of Technology\n\n- [2020-arxiv][STL] NENET: An Edge Learnable Network for Link Prediction in Scene Text [`paper`](https://arxiv.org/pdf/2005.12147.pdf)\n\n#### Xidian University\n\n- [2021-AAAI][STL][TR] PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network [`paper`](https://arxiv.org/pdf/2104.05458.pdf) [`code`](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/pgnet_en.md)\n- [2020-ICASSP][STL] Efficient Scene Text Detection with Textual Attention Tower [`paper`](https://arxiv.org/pdf/2002.03741.pdf)\n- [2019-ACM-MM][STL] A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning [`paper`](https://arxiv.org/pdf/1908.05498.pdf)\n\n#### Tongji University\n\n- [2019-AAAI][STL] Scene Text Detection with Supervised Pyramid Context Network [`paper`](https://arxiv.org/pdf/1811.08605.pdf) [`code`](https://github.com/AirBernard/Scene-Text-Detection-with-SPCNET)\n\n#### Harbin Institute of Technology\n\n- [2017-TIP][STL] Scene text detection and segmentation based on cascaded convolution neural networks (`paper`)[https://ieeexplore.ieee.org/document/7828014]\n\n#### Shanghai Jiao Tong University\n\n- [2018-ICPR][STL] Fused Text Segmentation Networks for Multi-oriented Scene Text Detection [`paper`](https://arxiv.org/pdf/1709.03272.pdf)  \n\n#### Ping An Property \u0026 Casualty Insurance\n\n- [2020-arxiv][TR] Hamming OCR: A Locality Sensitive Hashing Neural Network for Scene Text Recognition [`paper`](https://arxiv.org/pdf/2009.10874.pdf)\n\n#### Hefei University of Technology\n\n- [2020-arxiv][TR] Fast Dense Residual Network: Enhancing Global Dense Feature Flow for Text Recognition [`paper`](https://arxiv.org/pdf/2001.09021v1.pdf)\n\n#### Beihang University\n\n- [2020-arxiv][TR] A Feasible Framework for Arbitrary-Shaped Scene Text Recognition [`paper`](https://arxiv.org/pdf/1912.04561.pdf) [`code`](https:\n//github.com/zhang0jhon/AttentionOCR)\n\n#### Boston University\n\n- [2020-arxiv][TR] Deep Neural Network for Semantic-based Text Recognition in Images [`paper`](https://arxiv.org/pdf/1908.01403.pdf)\n\n#### Carnegie Mellon University\n\n- [2019-ICDAR][TR] Rethinking Irregular Scene Text Recognition [`paper`](https://arxiv.org/pdf/1908.11834.pdf) [`code`](https://github.com/Jyouhou/ICDAR2019-ArT-Recognition-Alchemy)\n\n#### Northwestern Polytechnical University\n\n- [2019-CVPR][STL][TR] Towards End-to-End Text Spotting in Natural Scenes [`paper`](https://arxiv.org/pdf/1906.06013.pdf)\n\n#### VinAI Research\n\n- [2021-CVPR][STL] Dictionary-Guided Scene Text Recognition [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Nguyen_Dictionary-Guided_Scene_Text_Recognition_CVPR_2021_paper.pdf) [`code`](https://github.com/VinAIResearch/dict-guided)\n\n#### University of Tokyo\n\n- [2021-CVPR][TR] What if We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labels [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Baek_What_if_We_Only_Use_Real_Datasets_for_Scene_Text_CVPR_2021_paper.pdf) [`code`](https://github.com/ku21fan/STR-Fewer-Labels)\n\n#### University of Surrey\n\n- [2021-ICCV][TR] Towards the Unseen: Iterative Text Recognition by Distilling from Errors [`paper`](https://openaccess.thecvf.com/content/ICCV2021/papers/Bhunia_Towards_the_Unseen_Iterative_Text_Recognition_by_Distilling_From_Errors_ICCV_2021_paper.pdf)\n- [2021-ICCV][TR] Joint Visual Semantic Reasoning: Multi-Stage Decoder for Text Recognition [`paper`](https://openaccess.thecvf.com/content/ICCV2021/papers/Bhunia_Joint_Visual_Semantic_Reasoning_Multi-Stage_Decoder_for_Text_Recognition_ICCV_2021_paper.pdf)\n- [2021-CVPR][TR] MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Bhunia_MetaHTR_Towards_Writer-Adaptive_Handwritten_Text_Recognition_CVPR_2021_paper.pdf)\n\n#### The Technion – Israel Institute of Technology\n\n- [2021-CVPR][TR] Sequence-to-Sequence Contrastive Learning for Text Recognition [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Aberdam_Sequence-to-Sequence_Contrastive_Learning_for_Text_Recognition_CVPR_2021_paper.pdf)\n\n#### University of Illinois at Urbana-Champaign\n\n- [2021-CVPR][TR] Rethinking Text Segmentation: A Novel Dataset and a Text-Specific Refinement Approach [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Rethinking_Text_Segmentation_A_Novel_Dataset_and_a_Text-Specific_Refinement_CVPR_2021_paper.pdf) [`code`](https://github.com/SHI-Labs/Rethinking-Text-Segmentation)\n\n#### National Laboratory of Pattern Recognition\n\n- [2021-CVPR][STL] Semantic-Aware Video Text Detection [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Feng_Semantic-Aware_Video_Text_Detection_CVPR_2021_paper.pdf)\n\n#### Shenzhen University\n\n- [2021-CVPR][STL][TR] Self-Attention Based Text Knowledge Mining for Text Detection [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Wan_Self-Attention_Based_Text_Knowledge_Mining_for_Text_Detection_CVPR_2021_paper.pdf) [`code`](https://github.com/CVI-SZU/STKM)\n\n#### University of the Philippines\n\n- [2021-ICDAR][TR] Vision Transformer for Fast and Efficient Scene Text Recognition [`paper`](https://arxiv.org/pdf/2105.08582.pdf) ['code'](https://github.com/roatienza/deep-text-recognition-benchmark)\n\n#### Beijing Jiaotong University\n\n- [2022-IJCAI][TR] SVTR: Scene Text Recognition with a Single Visual Model [`paper`](https://arxiv.org/pdf/2205.00159.pdf) [`code`](https://github.com/PaddlePaddle/PaddleOCR)\n\n#### Wuhan University\n\n- [2022-AAAI][TR] Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition [`paper`](https://arxiv.org/pdf/2112.12916.pdf) [`code`](https://github.com/adeline-cs/GTR)\n\n#### Helsing AI\n\n- [2022-WACV][TR] One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition [`paper`](https://openaccess.thecvf.com/content/WACV2022/papers/Souibgui_One-Shot_Compositional_Data_Generation_for_Low_Resource_Handwritten_Text_Recognition_WACV_2022_paper.pdf)\n\n#### Purdue University\n\n- [2023-WACV][TR] Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognition [`paper`](https://openaccess.thecvf.com/content/WACV2023/papers/Patel_Seq-UPS_Sequential_Uncertainty-Aware_Pseudo-Label_Selection_for_Semi-Supervised_Text_Recognition_WACV_2023_paper.pdf)\n\n## 2. Datasets\n\n#### [`SCUT-CTW1500`](https://github.com/Yuliang-Liu/Curve-Text-Detector) `2018`\n\nTask: text location(with different style) and recognition\n\n[`download`](https://github.com/Yuliang-Liu/Curve-Text-Detector)\n\n#### [`Total Text Dataset`](https://github.com/cs-chan/Total-Text-Dataset) `2017`\n\n1,555 images with more than 3 different text orientations: Horizontal, Multi-Oriented, and Curved, one of a kind\n\nTask: text location(with different style) and recognition\n\n[`download`](https://github.com/cs-chan/Total-Text-Dataset)\n\n#### [`PowerPoint Text Detection and Recognition Dataset`](https://gitlab.com/rex-yue-wu/ISI-PPT-Dataset) `2017`\n\n21,384 images, 21,384+ text instances\n\nTask: text location and recognition\n\n[`download`](https://gitlab.com/rex-yue-wu/ISI-PPT-Dataset)\n\n#### [`COCO-Text (Computer Vision Group, Cornell)`](http://vision.cornell.edu/se3/coco-text/)   `2016`\n\n63,686 images, 173,589 text instances, 3 fine-grained text attributes.\n\nTask: text location and recognition\n\n[`download`](https://github.com/andreasveit/coco-text)\n\n#### [`Synthetic Word Dataset (Oxford, VGG)`](http://www.robots.ox.ac.uk/~vgg/data/text/)   `2014`\n\n9 million images covering 90k English words\n\nTask: text recognition, segmantation\n\n[`download`](http://www.robots.ox.ac.uk/~vgg/data/text/mjsynth.tar.gz)\n\n#### [`The Street View House Number Dataset (SVHN)`](http://ufldl.stanford.edu/housenumbers)   `2012`\n\nReal-world street view number image with its position and classification tags.\n\nTask: number location detection, text recognition\n\n[`download`](http://ufldl.stanford.edu/housenumbers)\n\n#### [`IIIT 5K-Words`](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html)   `2012`\n\n5000 images from Scene Texts and born-digital (2k training and 3k testing images)\n\nEach image is a cropped word image of scene text with case-insensitive labels\n\nTask: text recognition\n\n[`download`](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K-Word_V3.0.tar.gz)\n\n#### [`StanfordSynth(Stanford, AI Group)`](http://cs.stanford.edu/people/twangcat/#research)   `2012`\n\nSmall single-character images of 62 characters (0-9, a-z, A-Z)\n\nTask: text recognition\n\n[`download`](http://cs.stanford.edu/people/twangcat/ICPR2012_code/syntheticData.tar)\n\n#### [`MSRA Text Detection 500 Database (MSRA-TD500)`](http://www.iapr-tc11.org/mediawiki/index.php/MSRA_Text_Detection_500_Database_(MSRA-TD500))   `2012`\n\n500 natural images(resolutions of the images vary from 1296x864 to 1920x1280)\n\nChinese, English or mixture of both\n\nTask: text detection\n\n#### [`Street View Text (SVT)`](http://tc11.cvc.uab.es/datasets/SVT_1)   `2010`\n\n350 high resolution images (average size 1260 × 860) (100 images for training and 250 images for testing)\n\nOnly word level bounding boxes are provided with case-insensitive labels\n\nTask: text location\n\n#### [`KAIST Scene_Text Database`](http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database)   `2010`\n\n3000 images of indoor and outdoor scenes containing text\n\nKorean, English (Number), and Mixed (Korean + English + Number)\n\nTask: text location, segmantation and recognition\n\n#### [`Chars74k`](http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/)   `2009`\n\nOver 74K images from natural images, as well as a set of synthetically generated characters\n\nSmall single-character images of 62 characters (0-9, a-z, A-Z)\n\nTask: text recognition\n\n#### `ICDAR Benchmark Datasets`\n\n|Dataset| Description | Competition Paper |\n|---|---|----|\n|[ICDAR 2017](http://rrc.cvc.uab.es/)| over 173,589 labeled text regions in over 63,686 images |`paper`  [![link](https://www.lds.org/bc/content/shared/content/images/gospel-library/manual/10735/paper-icon_1150845_tmb.jpg)](https://arxiv.org/abs/1601.07140)|\n|[ICDAR 2015](http://rrc.cvc.uab.es/)| 1000 training images and 500 testing images|`paper`  [![link](https://www.lds.org/bc/content/shared/content/images/gospel-library/manual/10735/paper-icon_1150845_tmb.jpg)](http://rrc.cvc.uab.es/files/Robust-Reading-Competition-Karatzas.pdf)|\n|[ICDAR 2013](http://dagdata.cvc.uab.es/icdar2013competition/)| 229 training images and 233 testing images |`paper`  [![link](https://www.lds.org/bc/content/shared/content/images/gospel-library/manual/10735/paper-icon_1150845_tmb.jpg)](http://dagdata.cvc.uab.es/icdar2013competition/files/icdar2013_competition_report.pdf)|\n|[ICDAR 2011](http://robustreading.opendfki.de/trac/)| 229 training images and 255 testing images |`paper`  [![link](https://www.lds.org/bc/content/shared/content/images/gospel-library/manual/10735/paper-icon_1150845_tmb.jpg)](http://www.iapr-tc11.org/archive/icdar2011/fileup/PDF/4520b491.pdf)|\n|[ICDAR 2005](http://www.iapr-tc11.org/mediawiki/index.php/ICDAR_2005_Robust_Reading_Competitions)| 1001 training images and 489 testing images |`paper`  [![link](https://www.lds.org/bc/content/shared/content/images/gospel-library/manual/10735/paper-icon_1150845_tmb.jpg)](http://www.academia.edu/download/30700479/10.1.1.96.4332.pdf)|\n|[ICDAR 2003](http://www.iapr-tc11.org/mediawiki/index.php/ICDAR_2003_Robust_Reading_Competitions)| 181 training images and 251 testing images(word level and character level) |`paper`  [![link](https://www.lds.org/bc/content/shared/content/images/gospel-library/manual/10735/paper-icon_1150845_tmb.jpg)](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.3461\u0026rep=rep1\u0026type=pdf)|\n\n## 3. Competitions\n\n- [ICDAR - Robust Reading Competitions](http://rrc.cvc.uab.es/?com=introduction)\n\n## 4. Online OCR Service\n\n| Name | Description |\n|---|----\n|[Tesseract OCR](https://github.com/tesseract-ocr/tesseract)| API，free |\n|[Online OCR](https://www.onlineocr.net/)| API，free |\n|[Free OCR](http://www.free-ocr.com/)| API，free |\n|[New OCR](http://www.newocr.com/)| API，free |\n|[ABBYY FineReader Online](https://finereaderonline.com)| No API，Not free |\n|[Super Online Transfer Tools (Chinese)](http://www.wdku.net/)| API，free |\n|[Online Chinese Recognition](http://chongdata.com/ocr/)| API，free |\n\n## 5. Blogs\n\n- [Scene Text Detection with OpenCV 3](http://docs.opencv.org/3.0-beta/modules/text/doc/erfilter.html)\n- [Handwritten numbers detection and recognition](https://medium.com/@o.kroeger/recognize-your-handwritten-numbers-3f007cbe46ff#.8hg7vl6mo)\n- [Applying OCR Technology for Receipt Recognition](http://rnd.azoft.com/applying-ocr-technology-receipt-recognition/)\n- [Convolutional Neural Networks for Object(Car License) Detection](http://rnd.azoft.com/convolutional-neural-networks-object-detection/)\n- [Extracting text from an image using Ocropus](http://www.danvk.org/2015/01/09/extracting-text-from-an-image-using-ocropus.html)\n- [Number plate recognition with Tensorflow](http://matthewearl.github.io/2016/05/06/cnn-anpr/) [`github`](https://github.com/matthewearl/deep-anpr)\n- [Using deep learning to break a Captcha system](https://deepmlblog.wordpress.com/2016/01/03/how-to-break-a-captcha-system/) [`report`](http://web.stanford.edu/~jurafsky/burszstein_2010_captcha.pdf) [`github`](https://github.com/arunpatala/captcha)\n- [Breaking reddit captcha with 96% accuracy](https://deepmlblog.wordpress.com/2016/01/05/breaking-reddit-captcha-with-96-accuracy/) [`github`](https://github.com/arunpatala/reddit.captcha)\n- [文字检测与识别资源-1](http://blog.csdn.net/peaceinmind/article/details/51387367)\n- [文字的检测与识别资源-2](http://blog.csdn.net/u010183397/article/details/56497303?locationNum=12\u0026fps=1)\n- Scene Text Recognition in iOS [`blog`](https://medium.com/@khurram.pak522/scene-text-recognition-in-ios-11-2d0df8412151) [`github`](https://github.com/khurram18/SceneTextRecognitioniOS)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwhitelok%2Fimage-text-localization-recognition","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwhitelok%2Fimage-text-localization-recognition","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwhitelok%2Fimage-text-localization-recognition/lists"}