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현재 언어: 한국어

Deep Text Detection and Recognition

Source: https://github.com/hwalsuklee/awesome-deep-text-detection-recognition at 0958b3875043
License: Apache-2.0
Read the English original

This edition is an AI-assisted translation.

딥러닝 기반 텍스트 검출 및 인식 논문을 엄선해 모은 목록입니다.

  • 논문은 발표 날짜순으로 정렬되어 있습니다.
  • IC는 ICDAR의 약자입니다.
  • 점수는 위치 검출 작업의 F1 점수입니다.
    • (L)은 리더보드의 점수를 뜻합니다.
    • 리더보드에 보고된 점수가 논문과 다소 다르면 (L) 점수를 함께 표시합니다.
  • *CODE는 공식 코드를, CODE(M)은 학습된 모델이 제공됨을 의미합니다.
학회날짜제목IC13IC15자료
‘14-ECCV14/10/07Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees
15-CVPR15/06/01Symmetry-based text line detection in natural scenes0.8043PRJ
CODE
‘16-TIP15/10/12Text-Attentional Convolutional Neural Networks for Scene Text Detection0.8165
‘15-ICCV15/12/13Text Flow : A Unified Text Detection System in Natural Scene Images0.8025
‘16-arXiv16/03/31Accurate Text Localization in Natural Image with Cascaded Convolutional TextNetwork0.86
‘16-CVPR16/04/14Multi-Oriented Text Detection with Fully Convolutional Networks0.830.54*TORCH(M)
‘16-CVPR16/04/22Synthetic Data for Text Localisation in Natural Images0.847
(L)0.8359
CODE
DB
‘16-arXiv16/06/29Scene Text Detection Via Holistic, Multi-Channel Prediction0.84330.6477
‘16-ECCV16/09/12Detecting Text in Natural Image with Connectionist Text Proposal Network0.82150.6085*CAFFE(M)
CAFFE
TF(M)
TF
DEMO
BLOG(CH)
‘17-AAAI16/11/21TextBoxes: A fast text detector with a single deep neural network0.85
(L)0.8767
*CAFFE(M)
TF
BLOG(KR)
‘18-TM17/03/03Arbitrary-Oriented Scene Text Detection via Rotation Proposals0.91250.8020*CAFFE
‘17-CVPR17/03/04Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection0.7064
‘17-CVPR17/03/19Detecting Oriented Text in Natural Images by Linking Segments0.8530.75
(L)0.7636
*TF(M)
TF(M)
SLIDE
VIDEO
‘17-arXiv17/03/24Deep Direct Regression for Multi-Oriented Scene Text Detection0.860.81
‘17-arXiv17/04/03Cascaded Segmentation-Detection Networks for Word-Level Text Spotting0.860.71
‘17-CVPR17/04/11EAST: An Efficient and Accurate Scene Text Detector0.8072
(L)0.8038
TF(M)
TF
PYTORCH(M)
PYTORCH
DEMO
KERAS(M)
VIDEO
‘17-ICIP17/05/15WordFence: Text Detection in Natural Images with Border Awareness0.86
‘17-arXiv17/06/30R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection0.87730.8254TF(M)
CAFFE(M)
‘17-CVPR17/07/21Multi-scale FCN with Cascaded Instance Aware Segmentation for Arbitrary Oriented Word Spotting In The Wild0.850.63
‘17-arXiv17/08/17Deep Scene Text Detection with Connected Component Proposals0.919
‘17-ICCV17/08/22WordSup: Exploiting Word Annotations for Character based Text Detection0.90640.7816
‘17-ICCV17/09/01Single Shot Text Detector with Regional Attention0.87040.7691*CAFFE(M)
PYTORCH
VIDEO
‘17-arXiv17/09/11Fused Text Segmentation Networks for Multi-oriented Scene Text Detection0.8414
‘17-ICCV17/10/13WeText: Scene Text Detection under Weak Supervision0.869
(L)0.8313
‘17-ICCV17/10/22Self-organized Text Detection with Minimal Post-processing via Border Learning0.84*KERAS(M)
‘17-ICDAR17/11/11Deep Residual Text Detection Network for Scene Text0.9117
(L)0.8925
‘18-AAAI17/11/12Feature Enhancement Network: A Refined Scene Text Detector0.9161
‘17-arXiv17/11/30ArbiText: Arbitrary-Oriented Text Detection in Unconstrained Scene0.759
‘18-AAAI18/01/04PixelLink: Detecting Scene Text via Instance Segmentation0.8810.8519*TF(M) TF
‘18-CVPR18/01/05FOTS: Fast Oriented Text Spotting with a Unified Network0.9250.8984PYTORCH
PYTORCH
VIDEO
‘18-TIP18/01/09TextBoxes++: A Single-Shot Oriented Scene Text Detector0.880.829
(L)0.8475
*CAFFE(M)
‘18-CVPR18/02/27Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation0.880.843*PYTORCH(M)
‘18-CVPR18/03/09An end-to-end TextSpotter with Explicit Alighment and Attention0.90.87*CAFFE(M)
‘18-CVPR18/03/14Rotation-Sensitive Regression for Oriented Scene Text Detection0.890.838*CAFFE(M)
‘18-arXiv18/04/08Detecting Multi-Oriented Text with Corner-based Region Proposals0.8760.845*CAFFE(M)
‘18-arXiv18/04/24An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches0.920.86
‘18-IJCAI18/05/03IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection0.9047
‘18-arXiv18/06/07Shape Robust Text Detection with Progressive Scale Expansion Network0.8721PRJ
‘18-ECCV18/07/04TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes0.826PYTORCH
‘18-ECCV18/07/06Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes0.9170.86
‘18-ECCV18/07/10Accurate Scene Text Detection through Border Semantics Awareness and Bootstrapping0.892
‘19-AAAI18/11/21Scene Text Detection with Supervised Pyramid Context Network0.9210.872
‘19-TIP18/12/04TextField: Learning A Deep Direction Field for Irregular Scene Text Detection0.824*CAFFE(M)
‘19-CVPR19/03/21Towards Robust Curve Text Detection with Conditional Spatial Expansion
‘19-CVPR19/03/28Shape Robust Text Detection with Progressive Scale Expansion Network0.857TF(M)
‘19-CVPR19/04/03Character Region Awareness for Text Detection0.9520.869*PYTORCH(M)
VIDEO
PYTORCH
TF(M)
KERAS
BLOG_CH
BLOG_KR
BLOG_KR
BLOG_KR
‘19-CVPR19/04/13Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes Screen reader support enabled0.877
‘19-CVPR19/06/16Learning Shape-Aware Embedding for Scene Text Detection0.877
‘19-CVPR19/06/16Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation0.9170.876
‘19-ICCV19/08/16Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network0.829
‘19-ICCV19/09/02Geometry Normalization Networks for Accurate Scene Text Detection0.8852
‘19-AAAI19/11/20Real-time Scene Text Detection with Differentiable Binarization0.847

  • 논문은 발표 날짜순으로 정렬되어 있습니다.
  • IC는 ICDAR의 약자입니다.
  • 점수는 인식 작업의 단어 정확도입니다.
    • IC03, IC13, IC15 데이터세트 결과에서 논문마다 사용한 샘플 수가 달랐지만,
      이를 구분하지 않았습니다.
  • *CODE 는 공식 코드를, CODE(M)은 학습된 모델이 제공됨을 의미합니다.
학회날짜제목SVTIIIT5kIC03IC13자료
‘15-ICLR14/12/18Deep structured output learning for unconstrained text recognition0.7170.8960.818TF
SLIDE
VIDEO
‘16-IJCV15/05/07Reading text in the wild with convolutional neural networks0.8070.9330.908KERAS
‘16-AAAI15/06/14Reading Scene Text in Deep Convolutional Sequences
‘17-TPAMI15/07/21An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition0.8080.7820.8940.867TORCH(M)
TF
TF
TF
TF
PYTORCH
PYTORCH(M)
BLOG(KR)
‘16-CVPR16/03/09Recursive Recurrent Nets with Attention Modeling for OCR in the Wild0.8070.7840.8870.9
‘16-CVPR16/03/12Robust scene text recognition with automatic rectification0.8190.8190.9010.886PYTORCH
PYTORCH
‘16-CVPR16/06/27CNN-N-Gram for Handwriting Word Recognition0.8362VIDEO
‘16-BMVC16/09/19STAR-Net: A SpaTial Attention Residue Network for Scene Text Recognition0.8360.8330.8990.891
‘17-arXiv17/07/27STN-OCR: A single Neural Network for Text Detection and Text Recognition0.7980.860.903*MXNET(M)
PRJ
BLOG
‘17-IJCAI17/08/19Learning to Read Irregular Text with Attention Mechanisms
‘17-arXiv17/09/06Scene Text Recognition with Sliding Convolutional Character Models0.7650.8160.8450.852
‘17-ICCV17/09/07Focusing Attention: Towards Accurate Text Recognition in Natural Images0.8590.8740.9420.933
‘18-CVPR17/11/12AON: Towards Arbitrarily-Oriented Text Recognition0.8280.870.915TF
‘17-NIPS17/12/04Gated Recurrent Convolution Neural Network for OCR0.8150.8080.978*TORCH(M)
‘18-AAAI18/01/04Char-Net: A Character-Aware Neural Network for Distorted Scene Text Recognition0.8440.8360.9150.908
‘18-AAAI18/01/04SqueezedText: A Real-time Scene Text Recognition by Binary Convolutional Encoder-decoder Network0.870.9310.929
‘18-CVPR18/05/09Edit Probability for Scene Text Recognition0.8750.8830.9460.944
‘18-TPAMI18/06/25ASTER: An Attentional Scene Text Recognizer with Flexible Rectification0.9360.9340.9450.918*TF(M)
PYTORCH
‘18-ECCV18/09/08Synthetically Supervised Feature Learning for Scene Text Recognition0.8710.8940.9470.94
‘19-AAAI18/09/18Scene Text Recognition from Two-Dimensional Perspective0.8210.920.914
‘19-AAAI18/11/02Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition0.8450.9150.91*TORCH(M)
‘19-CVPR18/12/14ESIR: End-to-end Scene Text Recognition via Iterative Image Rectification0.9020.9330.913PRJ
‘19-PR19/01/10MORAN: A Multi-Object Rectified Attention Network for Scene Text Recognition0.8830.9120.9500.924*PYTORCH(M)
‘19-ICCV19/04/03What is wrong with scene text recognition model comparisons? dataset and model analysis0.8750.9490.936*PYTORCH(M)
BLOG_KR
‘19-CVPR19/04/18Aggregation Cross-Entropy for Sequence Recognition0.8260.8230.9210.897*PYTORCH
‘19-CVPR19/06/16Sequence-to-Sequence Domain Adaptation Network for Robust Text Image Recognition0.8450.8380.9210.918
‘19-ICCV19/08/06Symmetry-constrained Rectification Network for Scene Text Recognition0.8890.9440.950.939
‘20-AAAI19/12/28TextScanner: Reading Characters in Order for Robust Scene Text Recognition0.8950.9260.925
‘20-AAAI19/12/21Decoupled Attention Network for Text Recognition0.8920.9430.950.939*PYTORCH(M)
‘20-AAAI20/02/04GTC: Guided Training of CTC0.9290.9550.9520.943

  • 논문은 발표 날짜순으로 정렬되어 있습니다.
  • IC는 ICDAR의 약자입니다.
  • 점수는 일반 작업의 F1 점수입니다.
  • *CODE 는 공식 코드를, CODE(M)은 학습된 모델이 제공됨을 의미합니다.
학회날짜제목IC03IC13IC15자료
‘12-ICPR12/11/11End-to-end text recognition with convolutional neural networks0.67*CODE
‘14-ECCV14/09/06Deep Features for Text Spotting0.75PRJ
MATLAB
‘15-IJCV15/05/07Reading Text in the Wild with Convolutional Neural Networks0.700.77KERAS
‘15-TPAMI15/10/30Real-time Lexicon-free Scene Text Localization and Recognition0.5420.156
‘16-arXiv16/04/10TextProposals: a Text-specific Selective Search Algorithm for Word Spotting in the Wild0.68430.4718
(L)0.533
*CAFFE(M)
‘17-AAAI16/11/21TextBoxes: A fast text detector with a single deep neural network0.84TF
*CAFFE(M)
BLOG_KR
‘17-ICCV17/07/13Towards End-to-end Text Spotting with Convolution Recurrent Neural Network0.8459VIDEO
‘17-ICCV17/10/22Deep TextSpotter An End-to-End Trainable Scene Text Localization and Recognition Framework0.770.47VIDEO
*CAFFE(M)
‘18-CVPR18/01/05FOTS: Fast Oriented Text Spotting with a Unified Network0.84770.6533VIDEO
TF(M)
‘18-TIP18/01/09TextBoxes++: A Single-Shot Oriented Scene Text Detector0.84650.519*CAFFE(M)
‘18-CVPR18/03/09An end-to-end TextSpotter with Explicit Alignment and Attention0.860.63*CAFFE(M)
‘18-TPAMI18/06/25ASTER: An Attentional Scene Text Recognizer with Flexible Rectification0.64*TF(M)
‘18-ECCV18/07/06Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes0.8650.624
‘19-ICCV19/08/24Towards Unconstrained End-to-End Text Spotting0.6994BLOG_KR
‘19-ICCV19/10/17Convolutional Character Networks0.7108*PYTORCH(M)
‘19-ICCV19/10/27TextDragon: An End-to-End Framework for Arbitrary Shaped Text Spotting0.6537
‘20-AAAI19/11/21All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting0.8410.641
‘20-AAAI20/02/12Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting0.8580.651

  • 논문은 발표 날짜순으로 정렬되어 있습니다.
  • *CODE 는 공식 코드를, CODE(M)은 학습된 모델이 제공됨을 의미합니다.
학회날짜제목설명자료
‘14-NIPS14/06/09Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition데이터세트PRJ
‘17-ECCV17/02/13End-to-End Interpretation of the French Street Name Signs Dataset데이터세트(FSNS)*TF(M)
‘17-arXiv17/04/11Attention-based Extraction of Structured Information from Street View ImageryFSNS*TF(M)
TF
TF
LUA
BLOG_KR
‘17-CVPR17/07/21Unambiguous Text Localization and Retrieval for Cluttered Scenes텍스트 검색
‘17-AAAI17/10/22Detection and Recognition of Text Embedded in Online Images via Neural Context Models데이터세트PRJ
‘18-CVPR17/11/17Separating Style and Content for Generalized Style Transfer글꼴 스타일
‘17-arXiv17/12/06Detecting Curve Text in the Wild New Dataset and New Solution데이터세트(CTW 1500)PRJ
‘18-AAAI17/12/14SEE: Towards Semi-Supervised End-to-End Scene Text RecognitionFSNSPRJ
*CHAINER(M)
‘17-CVPR18/06/07Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Networks문서 레이아웃PRJ
‘18-CVPR18/06/19DocUNet: Document Image Unwarping via A Stacked U-Net문서 왜곡 보정PRJ
‘18-CVPR18/06/19Document Enhancement using Visibility Detection문서 개선PRJ
‘18-IJCAI18/06/22Multi-Task Handwritten Document Layout Analysis문서 레이아웃
‘18-ECCV18/07/09Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes데이터세트PRJ
‘19-AAAI18/12/03EnsNet: Ensconce Text in the Wild텍스트 제거DB
‘19-CVPR18/12/14Spatial Fusion GAN for Image Synthesis데이터세트DB
‘19-AAAI19/01/27Hierarchical Encoder with Auxiliary Supervision for Table-to-text Generation: Learning Better Representation for Tables표-텍스트 변환
‘19-AAAI19/01/27A Radical-aware Attention-based Model for Chinese Text Classification한자 분류
‘19-CVPR19/02/25Handwriting Recognition in Low-resource Scripts using Adversarial Learning필기 인식TF
‘19-CVPR19/03/27Tightness-aware Evaluation Protocol for Scene Text Detection평가CODE
‘19-ICCV19/05/31Scene Text Visual Question Answering데이터세트ICDAR_DB
‘19-CVPR19/06/16DynTypo: Example-based Dynamic Text Effects Transfer텍스트 효과PRJ
VIDEO
‘19-CVPR19/06/16Typography with Decor: Intelligent Text Style Transfer텍스트 효과*PYTORCH(M)
‘19-CVPR19/06/16An Alternative Deep Feature Approach to Line Level Keyword Spotting키워드 검출
‘19-ICCV19/07/23GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition도메인 적응
‘19-ICCV19/09/17Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning데이터세트ICDAR_DB
‘19-ICCV19/10/02Large-scale Tag-based Font Retrieval with Generative Feature Learning글꼴 검색
‘19-ICCV19/10/27TextPlace: Visual Place Recognition and Topological Localization Through Reading Scene Texts장소 인식DB
‘19-ICCV19/10/27DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks문서 왜곡 보정*PYTORCH(M)
  • 이 작업은 NAVER-LINE이 지원하는 Clova AI의 OCR 팀에서 수행했습니다. NAVER-LINE은 클라우드 기반 AI 비서 플랫폼 Clova를 개발하는 아시아의 주요 인터넷 기업입니다.
  • 이 저장소는 주요 AI 학회 일정에 맞춰 정기적으로 업데이트될 예정입니다.

HagiCode

HagiCode는 에이전트 코딩 작업 공간입니다. 풍부하고 편리한 UI 상호작용으로 계획 수립, 에이전트 실행, 프로젝트 작업을 쉽게 시작하고 따라가며 개발을 더 흥미로운 경험으로 만듭니다.

풍부하고 편리한 UI 상호작용으로 계획부터 프로젝트 작업까지 쉽게 따라갈 수 있습니다.

HagiCode 방문문서 읽기
  • 스마트OpenSpec 워크플로는 의도를 제안, 설계, 작업을 거쳐 배포까지 이어지는 실행 가능한 경로로 바꿉니다.
  • 효율적다중 에이전트 실행으로 조사, 구현, 검토를 병렬로 진행하며 여러 저장소와 익숙한 Agent CLI를 지원합니다.
  • 흥미로운풍부하고 편리한 UI 상호작용으로 계획 수립, 에이전트 실행, 프로젝트 작업을 쉽게 시작하고 따라갈 수 있습니다.

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Microsoft Store에서 받기모든 다운로드

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HagiCode는 에이전트 코딩 작업 공간입니다. OpenSpec 워크플로로 변경을 계획하고, 다중 에이전트 실행으로 진행하며, Hero Scenario로 긴 작업의 진행 상황을 한눈에 보여 아이디어를 실제로 배포되는 소프트웨어로 만듭니다.

스마트
OpenSpec 워크플로는 의도를 제안부터 배포까지 이어지는 실행 가능한 경로로 바꿉니다.
효율적
다중 에이전트 실행으로 조사, 구현, 검토를 병렬로 진행합니다.
즐거운
편리한 UI 상호작용으로 계획, 에이전트 실행, 프로젝트 작업을 쉽게 따라갈 수 있습니다.

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