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- Readme: README.md
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README

        # End-to-End Scene Text Detection and Recognition System Resources

Author: Canjie Luo, Chongyu Liu


- [1.Datasets](#1-datasets)

    - [1.1 Introduction](#1-1intro)

    - [1.2 Comparison of Datasets](#1-2-dataset)

- [2. Summary of End-to-end Scene Text Detection and Recognition Methods](#2-summary-of-end2end-results)

    - [2.1 Comparison of methods](#21-comparison-of-methods)

    - [2.2 End-to-end scene text detection and recognition results](#22-end2end-result)

- [3. Survey](#3-field-survey)

- [4. OCR Service](#4-ocr-service)

- [5. References and codes](#5-references)

------



## 1. Datasets



### 1.1 Introduction

- SVT [15]：

  * **Introduction:** There are 100 training images and 250 testing images downloaded from Google Street View of road-side scenes. The labelled text can be very challenging with a wide variety of fonts, orientations, and lighting conditions. A lexicon containing 50 words (SVT-50) is also provided for each image.

  * **Link:** [SVT-download](http://vision.ucsd.edu/~kai/grocr/)

- ICDAR 2003(IC03) [16]：

  * **Introduction:** The dataset contains a varied array of photos of the world that contain scene text. There are 251 testing images with 50 word lexicons (IC03-50) and a lexicon of all test groundtruth words (IC03-Full).

  * **Link:** [IC03-download](http://www.iapr-tc11.org/mediawiki/index.php?title=ICDAR_2003_Robust_Reading_Competitions)

- ICDAR 2011(IC11) [17] :

  * **Introduction:** The dataset is an extension to the dataset used for the text locating competitions of ICDAR 2003.It includes 485 natural images in total.

  * **Link:** [IC11-download](http://www.cvc.uab.es/icdar2011competition/?com=downloads)   

- ICDAR 2013(IC13) [18]：

  * **Introduction:** The dataset consists of 229 training images and 233 testing images. Most text are horizontal. Three speciﬁc lexicons are provided, named as “Strong(S)”, “Weak(W)” and “Generic(G)”. “Strong(S)” lexicon provides 100 words per-image including all words that appear in the image. “Weak(W)” lexicon includes all words that appear in the entire test set. And “Generic(G)” lexicon is a 90k word vocabulary.

  * **Link:** [IC13-download](http://dagdata.cvc.uab.es/icdar2013competition/?ch=2&com=downloads)

- ICDAR 2015(IC15) [19]：

  - **Introduction:** The dataset includes 1000 training images and 500 testing images captured by Google glasses. The text in the scene is in arbitrary orientations. Similar to ICDAR 2013, it also provides “Strong(S)”, “Weak(W)” and “Generic(G)” lexicons.

  - **Link:** [IC15-download](http://rrc.cvc.uab.es/?ch=4&com=downloads)

- Total-Text [20]：

  - **Introduction:** Except for the horizontal text and oriented text, Total-Text also consists of a lot of curved text. Total-Text contains 1255 training images and 300 test images. All images are annotated with polygons and transcriptions in word-level. A “Full” lexicon contains all words in test set is provided.

  - **Link:** [Total-Text-download](https://github.com/cs-chan/Total-Text-Dataset)



### 1.2 Comparison of Datasets

	

	

		Comparison of Datasets

		

	

		Datasets

		Language

		Image

		Text instance 

		Text Shape

		Annotation level

		Lexicon

		

	

		Total

		Train

		Test

		Total

		Train

		Test

		Horizontal

		Arbitrary-Quadrilateral

		Multi-oriented

		Char

		Word

		Text-Line

		50

		1k

		Full

		None

	

	

		IC03

		English

		509

		258

		251

		2266

		1110

		1156

		✓

		✕

		✕

		✕

		✓

		✕

		✓

		✓

		✓

		✕

	

	

		IC11

		English

		484

		229

		255

		1564

		～

		～

		✓

		✕

		✕

		✓

		✓

		✕

		✕

		✕

		✕

		✓

	

	

		IC13

		English

		462

		229

		233

		1944

		849

		1095

		✓

		✕

		✕

		✓

		✓

		✕

		✕

		✕

		✕

		✓

	

	

		SVT

		English

		350

		100

		250

		725

		211

		514

		✓

		✓

		✕

		✓

		✓

		✕

		✓

		✕

		✕

		✕

	

	

		SVT-P

		English

		238

		～

		～

		639

		～

		～

		✓

		✓

		✕

		✕

		✓

		✕

		✓

		✕

		✓

		✕

	

	

		IC15

		English

		1500

		1000

		500

		17548

		122318

		5230

		✓

		✓

		✕

		✕

		✓

		✕

		✕

		✕

		✕

		✓

	

	

		Total-Text

		English

		1525

		1225

		300

		9330

		～

		～

		✓

		✓

		✓

		✕

		✓

		✓

		✕

		✕

		✕

		✓

	



## 2. Summary of End-to-end Scene Text Detection and Recognition Methods



### 2.1 Comparison of methods

	

	

	

	

	

	

	

	

		Method                    

		Model    

		Code

		Detection                        

		Recognition            

		Source 

		Time

		Highlight                                                            

	

	

		Wang et al. [1]

		


		✕

		Sliding windows and Random Ferns

		Pictorial Structures

		ICCV

		2011

		Word Re-scoring for NMS

	

	

		Wang et al. [2]

		


		✕

		CNN-based

		Sliding windows for classification

		ICPR

		2012

		CNN architecture

	

	

		Jaderberg et al. [3]

		


		✕

		CNN-based and saliency maps

		CNN classifier

		ECCV

		2014

		Data mining and annotation

	

	

		Alsharif et al. [4]

		


		✕

		CNN and hybrid HMM maxout models

		Segmentation-based

		ICLR

		2014

		Hybrid HMM maxout models

	

	

		Yao et al. [5]

		


		✕

		Random Forest

		Component Linking and Word Partition

		TIP

		2014

		(1) Detection and recognition features sharing. (2) Oriented-text. (3) A new dictionary search method

	

	

		Neumann et al. [6]

		


		✕

		Extremal Regions

		Clustering algorithm to group characters

		TPAMI

		2015

		Real-time performance(1.6s/image)

	

	

		Jaderberg et al. [7]

		


		✕

		Region proposal mechanism

		Word-level classification

		IJCV

		2016

		Trained only on data produced by a synthetic text generation engine, requiring no human labelled data

	

	

		Liao et al. [8]

		TextBoxes

		✓

		SSD-based framework

		CRNN

		AAAI

		2017

		An end-to-end trainable fast scene text detector

	

	

		Bŭsta et al. [9]

		Deep TextSpotter

		✕

		Yolo v2

		CTC

		ICCV

		2017

		Yolov2 + RPN, RNN + CTC. It is the first end-to-end trainable detection and recognition system with high speed.

	

	

		Li et al. [10]

		


		✕

		Text Proposal Network

		Attention

		ICCV

		2017

		TPN + RNN encoder + attention-based RNN

	

	

		Sun et al. [22]

		TextNet 

		✕

		Scale-aware attention backbone and Perspective RoI Transform 

		Attention

		ACCV

		2018

		Perspective RoI Transform for Irregular text recognition

	

	

		Lyu et al. [11]

		Mask TextSpotter

		✓

		Fast R-CNN with mask branch

		Character segmentation

		ECCV

		2018

		Precise text detection and recognition are acquired via semantic segmentation

	

	

		He et al. [12]

		


		✓

		Text-Alignment Layer

		Attention

		CVPR

		2018

		Character attention mechanism: use character spatial information as explicit supervision

	

	

		Liu et al. [13]

		FOTS

		✓

		EAST with RoIRotate

		CTC

		CVPR

		2018

		Little computation overhead compared to baseline text detection network (22.6fps)

	

	

		Liao et al. [14]

		TextBoxes++

		✓

		SSD-based framework

		CRNN

		TIP

		2018

		Journal version of TextBoxes (multi-oriented scene text support)

	

	

		Liao et al. [15]

		Mask TextSpotter

		✓

		Mask R-CNN

		Character segmentation + Spatial Attention Module

		TPAMI

		2019

		Journal version of Mask TextSpotter(proposes Spatial Attention Module)

	

	

		Xing et al. [23]

		CharNet

		✓

		A character branch and a detection branch 

		Character level

		ICCV

		2019

		Utilizing a character as basic element to overcome the main difficulty of joint optimization of  text detection and RNN-based recognition

	

	

		Feng et al. [24]

		TextDragon 

		✕

		Local box regression, center line segmentation and RoI Sliding

		CTC

		ICCV

		2019

		A new differentiable operator named RoISlide connect arbitrary shaped text detection and recognition

	

	

		Qin et al. [25]

		


		✕

		Mask R-CNN with RoI masking

		Attention

		ICCV

		2019

		A simple yet effective RoI masking step to extract useful irregularly shaped text instance features

	

	

		Qiao et al. [26]

		Text Perceptron 

		✕

		Mask R-CNN with Order-aware Semantic Segmentation and Boundary Regressions

		Attention

		AAAI

		2020

		A novel Shape Transform Module to transform the feature regions into regular morphologies

	

	

		Wang et al. [27]

		


		✕

		Oriented Rectangular Box Detector and Boundary Point Detector

		Attention

		AAAI

		2020

		A set of points on the boundary of each text instance represents arbitrary shapes

	

	

		Liu et al. [28]

		ABCNet

		✓

		Bezier Curve Detection and BezierAlign

		CTC

		CVPR

		2020

		10 times faster than re-cent state-of-the-art methods with a competitive scene text spotting accuracy

	



### 2.2 End-to-end scene text detection and recognition results

	

	

	

	

		      Method       

		Model

		Source 

		Time

		SVT

		SVT-50

		IC03

		IC11

		IC13

		IC15

		Total-text

		

	

		End-to-end

		Spotting

		End-to-end

		Spotting

		None

		Full

		None

		Full

		

	

		50

		Full

		None

		S

		W 

		G

		S

		W 

		G

		S

		W 

		G

		S

		W 

		G

		

	

		Wang et al. [1]

		


		ICCV

		2011

		~

		~

		51

		


		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

	

	

		Wang et al. [2]

		


		ICPR

		2012

		46

		~

		72

		67

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

	

	

		Jaderberg et al. [3]

		


		ECCV

		2014

		~

		56

		80

		75

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

	

	

		Alsharif et al. [4]

		


		ICLR

		2014

		~

		48

		77

		70

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

	

	

		Yao et al. [5]

		


		TIP

		2014

		~

		~

		~

		~

		


		48.6

		


		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

	

	

		Neumann et al. [6]

		


		TPAMI

		2015

		


		68.1

		~

		~

		~

		~

		45.2

		~

		~

		~

		~

		~

		35

		19.9

		15.6

		35

		19.9

		15.6

		~

		~

		~

		~

		~

	

	

		Jaderberg et al. [7]

		


		IJCV

		2016

		53

		76

		90

		86

		78

		76

		76

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

	

	

		Liao et al. [8]

		TextBoxes

		AAAI

		2017

		64

		84

		~

		~

		~

		87

		91

		89

		84

		94

		92

		87

		~

		~

		~

		~

		~

		~

		36.3

		48.9

		~

		~

		~

	

	

		Bŭsta et al. [9]

		Deep TextSpotter

		ICCV

		2017

		~

		~

		~

		~

		~

		~

		89

		86

		77

		92

		89

		81

		54

		51

		47

		58

		53

		51

		~

		~

		~

		~

		21.85

	

	

		Li et al. [10]

		


		ICCV

		2017

		66.18

		84.91

		~

		~

		~

		87.7

		~

		~

		~

		~

		~

		~

		91.08

		89.8

		84.6

		94.2

		92.4

		88.2

		~

		~

		~

		~

		~

	

	

		Sun et al. [22]

		TextNet 

		ACCV

		2018

		~

		~

		~

		~

		~

		~

		89.77

		88.80

		82.96

		94.59

		93.48

		86.99

		78.66

		74.9

		60.45

		82.38

		78.43

		62.36

		54.02

		~

		~

		~

		~

	

	

		Lyu et al. [11]

		Mask TextSpotter

		ECCV

		2018

		~

		~

		~

		~

		~

		~

		92.2

		91.1

		86.5

		92.5

		92

		88.2

		79.3

		73

		62.4

		79.3

		74.5

		64.2

		52.9

		71.8

		~

		~

		~

	

	

		He et al. [12]

		


		CVPR

		2018

		~

		~

		~

		~

		~

		~

		91

		89

		86

		93

		92

		87

		82

		77

		63

		85

		80

		65

		~

		~

		~

		~

		~

	

	

		Liu et al. [13]

		FOTS

		CVPR

		2018

		~

		~

		~

		~

		~

		~

		91.99

		90.11

		84.77

		95.94

		93.9

		87.76

		83.55

		79.11

		65.33

		87.01

		82.39

		67.97

		~

		~

		~

		~

		~

	

	

		Liao et al. [14]

		TextBoxes++

		TIP

		2018

		64

		84

		~

		~

		~

		~

		93

		92

		85

		96

		95

		87

		73.3

		65.9

		51.9

		76.5

		69

		54.4

		~

		~

		~

		~

		~

	

	

		Liao et al. [15]

		Mask TextSpotter

		TPAMI

		2019

		~

		~

		~

		~

		~

		~

		93.3

		91.3

		88.2

		92.7

		91.7

		87.7

		83

		77.7

		73.5

		82.4

		78.1

		73.6

		65.3

		77.4

		~

		~

		~

	

	

		Xing et al. [23]

		CharNet

		ICCV

		2019

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		85.05

		81.25

		71.08

		~

		~

		~

		69.2

		~

		~

		~

		~

	

	

		Feng et al. [24]

		TextDragon 

		ICCV

		2019

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		82.54

		78.34

		65.15

		86.22

		81.62

		68.03

		48.8

		74.8

		39.7

		72.4

		~

	

	

		Qin et al. [25]

		


		ICCV

		2019

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		85.51

		81.91

		69.94

		~

		~

		~

		70.7

		~

		~

		~

		~

	

	

		Qiao et al. [26]

		Text Perceptron 

		AAAI

		2020

		~

		~

		~

		~

		~

		~

		91.4

		90.7

		85.8

		94.9

		94

		88.5

		80.5

		76.6

		65.1

		84.1

		79.4

		67.9

		69.7

		78.3

		57

		~

		~

	

	

		Wang et al. [27]

		


		AAAI

		2020

		~

		~

		~

		~

		~

		~

		88.2

		87.7

		84.1

		~

		~

		~

		79.7

		75.2

		64.1

		~

		~

		~

		65

		76.1

		~

		~

		41.3

	

	

		Liu et al. [28]

		ABCNet

		CVPR

		2020

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		~

		69.5

		78.4

		45.2

		74.1

		~

	



## 3. Survey

**[A] \[TPAMI-2015]** Ye Q, Doermann D. **Text detection and recognition in imagery: A survey**[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(7): 1480-1500. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6945320)

**[B] \[Frontiers-Comput. Sci-2016]** Zhu Y, Yao C, Bai X. **Scene text detection and recognition: Recent advances and future trends**[J]. Frontiers of Computer Science, 2016, 10(1): 19-36. [paper](https://link.springer.com/article/10.1007/s11704-015-4488-0)

**[C] \[arXiv-2018]** Long S, He X, Ya C. **Scene Text Detection and Recognition: The Deep Learning Era**[J]. arXiv preprint arXiv:1811.04256, 2018. [paper](https://arxiv.org/pdf/1811.04256.pdf)



## 4. OCR Service

|                             OCR                              | API  | Free |

| :----------------------------------------------------------: | :--: | :--: |

| [Tesseract OCR Engine](https://github.com/tesseract-ocr/tesseract) |  ×   |  √   |

| [Azure](https://azure.microsoft.com/zh-cn/services/cognitive-services/computer-vision/#Analysis) |  √   |  √   |

| [ABBYY](https://www.abbyy.cn/real-time-recognition-sdk/technical-specifications/) |  √   |  √   |

|               [OCR Space](https://ocr.space/)                |  √   |  √   |

|       [SODA PDF OCR](https://www.sodapdf.com/ocr-pdf/)       |  √   |  √   |

|          [Free Online OCR](https://www.newocr.com/)          |  √   |  √   |

|           [Online OCR](https://www.onlineocr.net/)           |  √   |  √   |

|             [Super Tools](https://www.wdku.net/)             |  √   |  √   |

|          [Online Chinese Recognition](http://chongdata.com/ocr/)           |  √   |  √   |

|   [Calamari OCR](https://github.com/Calamari-OCR/calamari)   |  ×   |  √   |

|   [Tencent OCR](https://cloud.tencent.com/product/ocr?lang=cn)   |  √   |  ×   |



## 5. References and codes

- [1] Wang K, Babenko B, Belongie S. **End-to-end scene text recognition**[C].2011 International Conference on Computer Vision. IEEE, 2011: 1457-1464. [paper](http://www.iapr-tc11.org/dataset/SVT/wang_iccv2011.pdf)

- [2] Wang T, Wu D J, Coates A, et al. **End-to-end text recognition with convolutional neural networks**[C]. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012). IEEE, 2012: 3304-3308. [paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.664.6212&rep=rep1&type=pdf)

- [3] Jaderberg M, Vedaldi A, Zisserman A. **Deep features for text spotting**[C]. European conference on computer vision. Springer, Cham, 2014: 512-528. [paper](http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/jaderberg14deep.pdf)

- [4] Alsharif O, Pineau J. **End-to-End Text Recognition with Hybrid HMM Maxout Models**[C]. In ICLR 2014. [paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.740.1108&rep=rep1&type=pdf)

- [5] Yao C, Bai X, Liu W. **A unified framework for multioriented text detection and recognition**[J]. IEEE Transactions on Image Processing, 2014, 23(11): 4737-4749. [paper](http://www.vlrlab.net/admin/uploads/avatars/A_Unified_Framework_for_Multi-Oriented_Text_Detection_and_Recognition.pdf)

- [6] Neumann L, Matas J. **Real-time lexicon-free scene text localization and recognition**[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 38(9): 1872-1885. [paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.717.4947&rep=rep1&type=pdf)

- [7] Jaderberg M, Simonyan K, Vedaldi A, et al. **Reading text in the wild with convolutional neural networks**[J]. International Journal of Computer Vision, 2016, 116(1): 1-20. [paper](http://www.academia.edu/download/43938680/jaderberg16.pdf)

- [8] Liao M, Shi B, Bai X, et al. **Textboxes: A fast text detector with a single deep neural network**[C]. In AAAI 2017. [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/viewPDFInterstitial/14202/14295) [code](https://github.com/MhLiao/TextBoxes)

- [9] Busta M, Neumann L, Matas J. **Deep textspotter: An end-to-end trainable scene text localization and recognition framework**[C]. Proceedings of the IEEE International Conference on Computer Vision. 2017: 2204-2212. [paper](http://openaccess.thecvf.com/content_ICCV_2017/papers/Busta_Deep_TextSpotter_An_ICCV_2017_paper.pdf)

- [10] Li H, Wang P,  Shen C. **Towards end-to-end text spotting with convolutional recurrent neural networks**[C]. Proceedings of the IEEE International Conference on Computer Vision. 2017: 5238-5246. [paper](http://openaccess.thecvf.com/content_ICCV_2017/papers/Li_Towards_End-To-End_Text_ICCV_2017_paper.pdf)

- [11] Lyu P, Liao M, Yao C, et al. **Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes**[C]. Proceedings of the European Conference on Computer Vision (ECCV). 2018: 67-83. [paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Pengyuan_Lyu_Mask_TextSpotter_An_ECCV_2018_paper.pdf) [code](https://github.com/lvpengyuan/masktextspotter.caffe2)

- [12] He T, Tian Z, Huang W, et al. **An end-to-end textspotter with explicit alignment and attention**[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 5020-5029. [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)

- [13] Liu X, Liang D, Yan S, et al. **FOTS: Fast oriented text spotting with a unified network**[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 5676-5685. [paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_FOTS_Fast_Oriented_CVPR_2018_paper.pdf) [code](https://github.com/jiangxiluning/FOTS.PyTorch)

- [14] Liao M, Shi B, Bai X. **Textboxes++: A single-shot oriented scene text detector**[J]. IEEE transactions on image processing, 2018, 27(8): 3676-3690. [paper](https://ieeexplore.ieee.org/abstract/document/8334248) [code](https://github.com/MhLiao/TextBoxes_plusplus)

- [15] Minghui Liao, Pengyuan Lyu, Minghang He. **Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes**[J]. IEEE transactions on pattern analysis and machine intelligence, 2019. [paper](https://arxiv.org/abs/1908.08207) [code](https://github.com/MhLiao/TextBoxes_plusplus)

- [16] Wang,Kai, and S. Belongie. **Word Spotting in the Wild**. European Conference on Computer Vision(ECCV), 2010: 591-604.  [Paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.168.4897&rep=rep1&type=pdf)

- [17] S. M. Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong, R. Young,K. Ashida, H. Nagai, M. Okamoto, H. Yamamoto, H. Miyao,J. Zhu, W. Ou, C. Wolf, J. Jolion, L. Todoran, M. Worring, and X. Lin. **ICDAR 2003 robust reading competitions:entries, results,and future directions**. IJDAR, 7(2-3):105–122, 2005. [paper](https://link.springer.com/content/pdf/10.1007%2Fs10032-004-0134-3.pdf)

- [18] Shahab, A, Shafait, F, Dengel, A: **ICDAR 2011 robust reading competition challenge 2: Reading text in scene images**. In: ICDAR, 2011. [Paper](https://ieeexplore.ieee.org/document/6065556)

- [19] D. Karatzas, F. Shafait, S. Uchida, et al. **ICDAR 2013 robust reading competition**. In ICDAR, 2013. [Paper](https://ieeexplore.ieee.org/document/6628859)

- [20] D. Karatzas, L. Gomez-Bigorda, A. Nicolaou, S. K. Ghosh, A. D.Bagdanov, M. Iwamura, J. Matas, L. Neumann, V. R. Chandrasekhar, S. Lu, F. Shafait, S. Uchida, and E. Valveny. **ICDAR 2015 competition on robust reading**. In ICDAR, pages 1156–1160, 2015. [Paper](https://ieeexplore.ieee.org/document/7333942)

- [21] Chee C K, Chan C S. **Total-text: A comprehensive dataset for scene text detection and recognition**.Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on. IEEE, 2017, 1: 935-942.[Paper](https://arxiv.org/abs/1710.10400)

- [22] Y. Sun, C. Zhang, Z. Huang, J. Liu, J. Han, and E. Ding, **TextNet: Irregular Text Reading from Images with an End-to-End Trainable Network**, Asian Conference on Computer Vision (ACCV), Cham, 2018, vol. 11363, no. 1, pp. 83–99.[Paper](https://link.springer.com/chapter/10.1007/978-3-030-20893-6_6)

- [23] Xing L, Tian Z, Huang W, **Convolutional character networks**.In ICCV, 2019.[Paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Xing_Convolutional_Character_Networks_ICCV_2019_paper.pdf) [code](https://github.com/MalongTech/research-charnet)

- [24] Feng W, He W, Yin F, et al. **TextDragon: An End-to-End Framework for Arbitrary Shaped Text Spotting**.In ICCV, 2019.[Paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Feng_TextDragon_An_End-to-End_Framework_for_Arbitrary_Shaped_Text_Spotting_ICCV_2019_paper.pdf)

- [25] Qin S, Bissacco A, Raptis M, et al. **Towards unconstrained end-to-end text spotting**.In ICCV, 2019.[Paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Qin_Towards_Unconstrained_End-to-End_Text_Spotting_ICCV_2019_paper.pdf)

- [26] Qiao L, Tang S, Cheng Z, et al. **Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting**.In AAAI 2020.[Paper](https://www.aaai.org/Papers/AAAI/2020GB/AAAI-QiaoL.893.pdf)

- [27] Wang H, Lu P, Zhang H, et al. **All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting.** In AAAI 2020.[Paper](https://arxiv.org/abs/1911.09550)

- [28] Liu Y, Chen H, Shen C, et al. **ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network** In CVPR, 2020.[Paper](https://arxiv.org/abs/2002.10200)

[code](https://github.com/Yuliang-Liu/bezier_curve_text_spotting)

###

If you find any problems in our resources, or any good papers/codes we have missed, please inform us at    **[email protected]**. Thank you for your contribution.

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