Overview and Resources of Scene Text Detection

Scene Text Detection Resources

转自华南理工大学SCTU-DLVC实验室，原文链接https://github.com/HCIILAB/Scene-Text-Detection

Author: Chongyu Liu

• 1.Datasets
  • 1.1 Horizontal-Text Datasets
  • 1.2 Arbitrary-Quadrilateral-Text Datasets
  • 1.3 Irregular-Text Datasets
  • 1.4 Synthetic Datasets
  • 1.5 Comparison of Datasets
• 2. Summary of Scene Text Detection Resources
  • 2.1 Comparison of Methods
    • 2.1.1 Traditional Methods
    • 2.1.2 Segmentation-based Methods
    • 2.1.3 Regression-based Methods
    • 2.1.4 Hybrid Methods
  • 2.2 Detection Results
    • 2.2.1 Detection Results on Horizontal-Text Datasets
    • 2.2.2 Detection Results on Arbitrary-Quadrilateral-Text Datasets
    • 2.2.3 Detection Results on Irregular-Text Datasets
• 3. Survey
• 4. Evaluation
• 5. OCR Service
• 6. References and Code

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1. Datasets

1.1 Horizontal-Text Datasets

• ICDAR 2003(IC03)：

  • Introduction: It contains 509 images in total, 258 for training and 251 for testing. Specifically, it contains 1110 text instance in training set, while 1156 in testing set. It has word-level annotation. IC03 only consider English text instance.
  • Link: IC03-download

• ICDAR 2011(IC11):

  • Introduction: IC11 is an English dataset for text detection. It contains 484 images, 229 for training and 255 for testing. There are 1564 text instance in this dataset. It provides both word-level and character-level annotation.
  • Link: IC11-download

• ICDAR 2013(IC13)：

  • Introduction: IC13 is almost the same as IC11. It contains 462 images in total, 229 for training and 233 for testing. Specifically, it contains 849 text instance in training set, while 1095 in testing set.
  • Link: IC13-download

1.2 Arbitrary-Quadrilateral-Text Datasets

• USTB-SV1K：

  • Introduction: USTB-SV1K is an English dataset. It contains 1000 street images from Google Street View with 2955 text instance in total. It only provides word-level annotations.
  • Link: USTB-SV1K-download

• SVT：

  • Introduction: It contains 350 images with 725 English text intance in total. SVT has both character-level and word-level annotations. The images of SVT are harvested from Google Street View and have low resolution.
  • Link: SVT-download

• SVT-P：

  • Introduction: It contains 639 cropped word images for testing. Images were selected from the side-view angle snapshots in Google Street View. Therefore, most images are heavily distorted by the non-frontal view angle. It is the imporved datasets of SVT.
  • Link: SVT-P-download (Password : vnis)

• ICDAR 2015(IC15)：

  • Introduction: It contains 1500 images in total, 1000 for training and 500 for testing. Specifically, it contains 17548 text instance. It provides word-level annotations. IC15 is the first incidental scene text dataset and it only considers English words.
  • Link: IC15-download

• COCO-Text：

  • Introduction: It contains 63686 images in total, 43686 for training, 10000 for validating and 10000 for testing. Specifically, it contains 145859 cropped word images for testing, including handwritten and printed, clear and blur, English and non-English.
  • Link: COCO-Text-download

• MSRA-TD500：

  • Introduction: It contains 500 images in total. It provides text-line-level annotation rather than word, and polygon boxes rather than axis-aligned rectangles for text region annootation. It contains both English and Chinese text instance.
  • Link: MSRA-TD500-download

• MLT 2017：

  • Introduction: It contains 10000 natural images in total. It provides word-level annotation. There are 9 languages for MLT. It is a more real and complex datasets for scene text detection and recognition…
  • Link: MLT-download

• MLT 2019:

  • Introduction: It contains 18000 images in total. It provides word-level annotation. Compared to MLT, this dataset has 10 languages. It is a more real and complex datasets for scene text detection and recognition…
  • Link: MLT-2019-download

• CTW：

  • Introduction: It contains 32285 high resolution street view images of Chinese text, with 1018402 character instances in total. All images are annotated at the character level, including its underlying character type, bouding box, and 6 other attributes. These attributes indicate whether its background is complex, whether it’s raised, whether it’s hand-written or printed, whether it’s occluded, whether it’s distorted, whether it uses word-art.
  • Link: CTW-download

• RCTW-17：

  • Introduction: It contains 12514 images in total, 11514 for training and 1000 for testing. Images in RCTW-17 were mostly collected by camera or mobile phone, and others were generated images. Text instances are annotated with parallelograms. It is the first large scale Chinese dataset, and was also the largest published one by then.
  • Link: RCTW-17-download

• ReCTS：

  • Introduction: This data set is a large-scale Chinese Street View Trademark Data Set. It is based on Chinese words and Chinese text line-level labeling. The labeling method is arbitrary quadrilateral labeling. It contains 20000 images in total.
  • Link: ReCTS-download

1.3 Irregular-Text Datasets

• CUTE80：

  • Introduction: It contains 80 high-resolution images taken in natural scenes. Specifically, it contains 288 cropped word images for testing. The dataset focuses on curved text. No lexicon is provided.
  • Link: CUTE80-download

• Total-Text：

  • Introduction: It contains 1,555 images in total. Specifically, it contains 11,459 cropped word images with more than three different text orientations: horizontal, multi-oriented and curved.
  • Link: Total-Text-download

• SCUT-CTW1500：

  • Introduction: It contains 1500 images in total, 1000 for training and 500 for testing. Specifically, it contains 10751 cropped word images for testing. Annotations in CTW-1500 are polygons with 14 vertexes. The dataset mainly consists of Chinese and English.
  • Link: CTW-1500-download

• LSVT：

  • Introduction: LSVT consists of 20,000 testing data, 30,000 training data in full annotations and 400,000 training data in weak annotations, which are referred to as partial labels. The labeled text regions demonstrate the diversity of text: horizontal, multi-oriented and curved.
  • Link: LSVT-download

• ArT：

  • Introduction: ArT consists of 10,166 images, 5,603 for training and 4,563 for testing. They were collected with text shape diversity in mind and all text shapes have high number of existence in ArT.
  • Link: ArT-download

1.4 Synthetic Datasets

• Synth80k :

  • Introduction: It contains 800 thousands images with approximately 8 million synthetic word instances. Each text instance is annotated with its text-string, word-level and character-level bounding-boxes.
  • Link: Synth80k-download

• SynthText :

  • Introduction: It contains 6 million cropped word images. The generation process is similar to that of Synth90k. It is also annotated in horizontal-style.
  • Link: SynthText-download

1.5 Comparison of Datasets

Comparison of Datasets

Datasets

Language

Image

Text instance

Text Shape

Annotation level

Total

Train

Test

Total

Train

Test

Horizontal

Arbitrary-Quadrilateral

Multi-oriented

Char

Word

Text-Line

IC03

English

509

258

251

2266

1110

1156

✓

✕

✕

✕

✓

✕

IC11

English

484

229

255

1564

～

～

✓

✕

✕

✓

✓

✕

IC13

English

462

229

233

1944

849

1095

✓

✕

✕

✓

✓

✕

USTB-SV1K

English

1000

500

500

2955

～

～

✓

✓

✕

✕

✓

✕

SVT

English

350

100

250

725

211

514

✓

✓

✕

✓

✓

✕

SVT-P

English

238

～

～

639

～

～

✓

✓

✕

✕

✓

✕

IC15

English

1500

1000

500

17548

122318

5230

✓

✓

✕

✕

✓

✕

COCO-Text

English

63686

43686

20000

145859

118309

27550

✓

✓

✕

✕

✓

✕

MSRA-TD500

English/Chinese

500

300

200

～

～

～

✓

✓

✕

✕

✕

✓

MLT 2017

Multi-lingual

18000

7200

10800

～

～

～

✓

✓

✕

✕

✓

✕

MLT 2019

Multi-lingual

20000

10000

10000

～

～

～

✓

✓

✕

✕

✓

✕

CTW

Chinese

32285

25887

6398

1018402

812872

205530

✓

✓

✕

✓

✓

✕

RCTW-17

English/Chinese

12514

15114

1000

～

～

～

✓

✓

✕

✕

✕

✓

ReCTS

Chinese

20000

～

～

～

～

～

✓

✓

✕

✓

✓

✕

CUTE80

English

80

～

～

～

～

～

✕

✕

✓

✕

✓

✓

Total-Text

English

1525

1225

300

9330

～

～

✓

✓

✓

✕

✓

✓

CTW-1500

English/Chinese

1500

1000

500

10751

～

～

✓

✓

✓

✕

✓

✓

LSVT

English/Chinese

450000

430000

20000

～

～

～

✓

✓

✓

✕

✓

✓

ArT

English/Chinese

10166

5603

4563

～

～

～

✓

✓

✓

✕

✓

✕

Synth80k

English

80k

～

～

8m

～

～

✓

✕

✕

✓

✓

✕

SynthText

English

800k

～

～

6m

～

～

✓

✓

✕

✕

✓

✕

2. Summary of Scene Text Detection Resources

2.1 Comparison of Methods

Scene text detection methods can be devided into four parts:

(a) Traditional methods;

(b) Segmentation-based methods;

© Regression-based methods;

(d) Hybrid methods.

It is important to notice that: (1) “Hori” stands for horizontal scene text datasets. (2) “Quad” stands for arbitrary-quadrilateral-text datasets. (3) “Irreg” stands for irregular scence text datasets. (4) “Traditional method” stands for the methods that don’t rely on deep learning.

2.1.1 Traditional Methods

Method	Model	Code	Hori	Quad	Irreg	Source	Time	Highlight
Yao et al. [1]	TD-Mixture	✕	✓	✓	✕	CVPR	2012	1) A new dataset MSRA-TD500 and protocol for evaluation. 2) Equipped a two-level classification scheme and two sets of features extractor.
Yin et al. [2]		✕	✓	✕	✕	TPAMI	2013	Extract Maximally Stable Extremal Regions (MSERs) as character candidates and group them together.
Le et al. [5]	HOCC	✕	✓	✓	✕	CVPR	2014	HOCC + MSERs
Yin et al. [7]		✕	✓	✓	✕	TPAMI	2015	Presenting a unified distance metric learning framework for adaptive hierarchical clustering.
Wu et al. [9]		✕	✓	✓	✕	TMM	2015	Exploring gradient directional symmetry at component level for smoothing edge components before text detection.
Tian et al. [17]		✕	✓	✕	✕	IJCAI	2016	Scene text is first detected locally in individual frames and finally linked by an optimal tracking trajectory.
Yang et al. [33]		✕	✓	✓	✕	TIP	2017	A text detector will locate character candidates and extract text regions. Then they will linked by an optimal tracking trajectory.
Liang et al. [8]		✕	✓	✓	✓	TIP	2015	Exploring maxima stable extreme regions along with stroke width transform for detecting candidate text regions.
Michal et al.[12]	FASText	✕	✓	✓	✕	ICCV	2015	Stroke keypoints are efficiently detected and then exploited to obtain stroke segmentations.

2.1.2 Segmentation-based Methods

Method	Model	Code	Hori	Quad	Irreg	Source	Time	Highlight
Li et al. [3]		✕	✓	✓	✕	TIP	2014	(1)develop three novel cues that are tailored for character detection and a Bayesian method for their integration; (2)design a Markov random field model to exploit the inherent dependencies between characters.
Zhang et al. [14]		✕	✓	✓	✕	CVPR	2016	Utilizing FCN for salient map detection and centroid of each character prediction.
Zhu et al. [16]		✕	✓	✓	✕	CVPR	2016	Performs a graph-based segmentation of connected components into words (Word-Graph).
He et al. [18]	Text-CNN	✕	✓	✓	✕	TIP	2016	Developing a new learning mechanism to train the Text-CNN with multi-level and rich supervised information.
Yao et al. [21]		✕	✓	✓	✕	arXiv	2016	Proposing to localize text in a holistic manner, by casting scene text detection as a semantic segmentation problem.
Hu et al. [27]	WordSup	✕	✓	✓	✕	ICCV	2017	Proposing a weakly supervised framework that can utilize word annotations. Then the detected characters are fed to a text structure analysis module.
Wu et al. [28]		✕	✓	✓	✕	ICCV	2017	Introducing the border class to the text detection problem for the first time, and validate that the decoding process is largely simplified with the help of text border.
Tang et al.[32]		✕	✓	✕	✕	TIP	2017	A text-aware candidate text region(CTR) extraction model + CTR refinement model.
Dai et al. [35]	FTSN	✕	✓	✓	✕	arXiv	2017	Detecting and segmenting the text instance jointly and simultaneously, leveraging merits from both semantic segmentation task and region proposal based object detection task.
Wang et al. [38]		✕	✓	✕	✕	ICDAR	2017	This paper proposes a novel character candidate extraction method based on super-pixel segmentation and hierarchical clustering.
Deng et al. [40]	PixelLink	✓	✓	✓	✕	AAAI	2018	Text instances are first segmented out by linking pixels wthin the same instance together.
Liu et al. [42]	MCN	✕	✓	✓	✕	CVPR	2018	Stochastic Flow Graph (SFG) + Markov Clustering.
Lyu et al. [43]		✕	✓	✓	✕	CVPR	2018	Detect scene text by localizing corner points of text bounding boxes and segmenting text regions in relative positions.
Chu et al. [45]	Border	✕	✓	✓	✕	ECCV	2018	The paper presents a novel scene text detection technique that makes use of semantics-aware text borders and bootstrapping based text segment augmentation.
Long et al. [46]	TextSnake	✕	✓	✓	✓	ECCV	2018	The paper proposes TextSnake, which is able to effectively represent text instances in horizontal, oriented and curved forms based on symmetry axis.
Yang et al. [47]	IncepText	✕	✓	✓	✕	IJCAI	2018	Designing a novel Inception-Text module and introduce deformable PSROI pooling to deal with multi-oriented text detection.
Yue et al. [48]		✕	✓	✓	✕	BMVC	2018	Proposing a general framework for text detection called Guided CNN to achieve the two goals simultaneously.
Zhong et al. [53]	AF-RPN	✕	✓	✓	✕	arXiv	2018	Presenting AF-RPN(anchor-free) as an anchor-free and scale-friendly region proposal network for the Faster R-CNN framework.
Wang et al. [54]	PSENet	✓	✓	✓	✓	CVPR	2019	Proposing a novel Progressive Scale Expansion Network (PSENet), designed as a segmentation-based detector with multiple predictions for each text instance.
Xu et al.[57]	TextField	✕	✓	✓	✓	arXiv	2018	Presenting a novel direction field which can represent scene texts of arbitrary shapes.
Tian et al. [58]	FTDN	✕	✓	✓	✕	ICIP	2018	FTDN is able to segment text region and simultaneously regress text box at pixel-level.
Tian et al. [83]		✕	✓	✓	✓	CVPR	2019	Constraining embedding feature of pixels inside the same text region to share similar properties.
Huang et al. [4]	MSERs-CNN	✕	✓	✕	✕	ECCV	2014	Combining MSERs with CNN
Sun et al. [6]		✕	✓	✕	✕	PR	2015	Presenting a robust text detection approach based on color-enhanced CER and neural networks.
Baek et al. [62]	CRAFT	✕	✓	✓	✓	CVPR	2019	Proposing CRAFT effectively detect text area by exploring each character and affinity between characters.

2.1.3 Regression-based Methods

Method	Model	Code	Hori	Quad	Irreg	Source	Time	Highlight
Gupta et al. [15]	FCRN	✓	✓	✕	✕	CVPR	2016	(a) Proposing a fast and scalable engine to generate synthetic images of text in clutter; (b) FCRN.
Zhong et al. [20]	DeepText	✕	✓	✕	✕	arXiv	2016	(a) Inception-RPN; (b) Utilize ambiguous text category (ATC) information and multilevel region-of-interest pooling (MLRP).
Liao et al. [22]	TextBoxes	✓	✓	✕	✕	AAAI	2017	Mainly basing SSD object detection framework.
Liu et al. [25]	DMPNet	✕	✓	✓	✕	CVPR	2017	Quadrilateral sliding windows + shared Monte-Carlo method for fast and accurate computing of the polygonal areas + a sequential protocol for relative regression.
He et al. [26]	DDR	✕	✓	✓	✕	ICCV	2017	Proposing an FCN that has bi-task outputs where one is pixel-wise classification between text and non-text, and the other is direct regression to determine the vertex coordinates of quadrilateral text boundaries.
Jiang et al. [36]	R2CNN	✕	✓	✓	✕	arXiv	2017	Using the Region Proposal Network (RPN) to generate axis-aligned bounding boxes that enclose the texts with different orientations.
Xing et al. [37]	ArbiText	✕	✓	✓	✕	arXiv	2017	Adopting the circle anchors and incorporating a pyramid pooling module into the Single Shot MultiBox Detector framework.
Zhang et al. [39]	FEN	✕	✓	✕	✕	AAAI	2018	Proposing a refined scene text detector with a novel Feature Enhancement Network (FEN) for Region Proposal and Text Detection Refinement.
Wang et al. [41]	ITN	✕	✓	✓	✕	CVPR	2018	ITN is presented to learn the geometry-aware representation encoding the unique geometric configurations of scene text instances with in-network transformation embedding.
Liao et al. [44]	RRD	✕	✓	✓	✕	CVPR	2018	The regression branch extracts rotation-sensitive features, while the classification branch extracts rotation-invariant features by pooling the rotation sensitive features.
Liao et al. [49]	TextBoxes++	✓	✓	✓	✕	TIP	2018	Mainly basing SSD object detection framework and it replaces the rectangular box representation in conventional object detector by a quadrilateral or oriented rectangle representation.
He et al. [50]		✕	✓	✓	✕	TIP	2018	Proposing a scene text detection framework based on fully convolutional network with a bi-task prediction module.
Ma et al. [51]	RRPN	✓	✓	✓	✕	TMM	2018	RRPN + RRoI Pooling.
Zhu et al. [55]	SLPR	✕	✓	✓	✓	arXiv	2018	SLPR regresses multiple points on the edge of text line and then utilizes these points to sketch the outlines of the text.
Deng et al. [56]		✓	✓	✓	✕	arXiv	2018	CRPN employs corners to estimate the possible locations of text instances. And it also designs a embedded data augmentation module inside region-wise subnetwork.
Cai et al. [59]	FFN	✕	✓	✕	✕	ICIP	2018	Proposing a Feature Fusion Network to deal with text regions differing in enormous sizes.
Sabyasachi et al. [60]	RGC	✕	✓	✓	✕	ICIP	2018	Proposing a novel recurrent architecture to improve the learnings of a feature map at a given time.
Liu et al. [63]	CTD	✓	✓	✓	✓	PR	2019	CTD + TLOC + PNMS
Xie et al. [79]	DeRPN	✓	✓	✕	✕	AAAI	2019	DeRPN utilizes anchor string mechanism instead of anchor box in RPN.
Wang et al. [82]		✕	✓	✓	✓	CVPR	2019	Text-RPN + RNN
Liu et al. [84]		✕	✓	✓	✓	CVPR	2019	CSE mechanism
He et al. [29]	SSTD	✓	✓	✓	✕	ICCV	2017	Proposing an attention mechanism. Then developing a hierarchical inception module which efficiently aggregates multi-scale inception features.
Tian et al. [11]		✕	✓	✕	✕	ICCV	2015	Cascade boosting detects character candidates, and the min-cost flow network model get the final result.
Tian et al. [13]	CTPN	✓	✓	✕	✕	ECCV	2016	1) RPN + LSTM. 2) RPN incorporate a new vertical anchor mechanism and LSTM connects the region to get the final result.
He et al. [19]		✕	✓	✓	✕	ACCV	2016	ER detetctor detects regions to get coarse prediction of text regions. Then the local context is aggregated to classify the remaining regions to obtain a final prediction.
Shi et al. [23]	SegLink	✓	✓	✓	✕	CVPR	2017	Decomposing text into segments and links. A link connects two adjacent segments.
Tian et al. [30]	WeText	✕	✓	✕	✕	ICCV	2017	Proposing a weakly supervised scene text detection method (WeText).
Zhu et al. [31]	RTN	✕	✓	✕	✕	ICDAR	2017	Mainly basing CTPN vertical vertical proposal mechanism.
Ren et al. [34]		✕	✓	✕	✕	TMM	2017	Proposing a CNN-based detector. It contains a text structure component detector layer, a spatial pyramid layer, and a multi-input-layer deep belief network (DBN).
Zhang et al. [10]		✕	✓	✕	✕	CVPR	2015	The proposed algorithm exploits the symmetry property of character groups and allows for direct extraction of text lines from natural images.

2.1.4 Hybrid Methods

Method	Model	Code	Hori	Quad	Irreg	Source	Time	Highlight
Tang et al. [52]	SSFT	✕	✓	✕	✕	TMM	2018	Proposing a novel scene text detection method that involves superpixel-based stroke feature transform (SSFT) and deep learning based region classification (DLRC).
Xie et al.[61]	SPCNet	✕	✓	✓	✓	AAAI	2019	Text Context module + Re-Score mechanism.
Liu et al. [64]	PMTD	✓	✓	✓	✕	arXiv	2019	Perform “soft” semantic segmentation. It assigns a soft pyramid label (i.e., a real value between 0 and 1) for each pixel within text instance.
Liu et al. [80]	BDN	✓	✓	✓	✕	IJCAI	2019	Discretizing bouding boxes into key edges to address label confusion for text detection.
Zhang et al. [81]	LOMO	✕	✓	✓	✓	CVPR	2019	DR + IRM + SEM
Zhou et al. [24]	EAST	✓	✓	✓	✕	CVPR	2017	The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images with instance segmentation.
Yue et al. [48]		✕	✓	✓	✕	BMVC	2018	Proposing a general framework for text detection called Guided CNN to achieve the two goals simultaneously.
Zhong et al. [53]	AF-RPN	✕	✓	✓	✕	arXiv	2018	Presenting AF-RPN(anchor-free) as an anchor-free and scale-friendly region proposal network for the Faster R-CNN framework.

2.2 Detection Results

2.2.1 Detection Results on Horizontal-Text Datasets

Method

Model

Source

Time

Method Category

IC11[68]

IC13 [69]

IC05[67]

P

R

F

P

R

F

P

R

F

Yao et al. [1]

TD-Mixture

CVPR

2012

Traditional

~

~

~

0.69

0.66

0.67

~

~

~

Yin et al. [2]

TPAMI

2013

0.86

0.68

0.76

~

~

~

~

~

~

Yin et al. [7]

TPAMI

2015

0.838

0.66

0.738

~

~

~

~

~

~

Wu et al. [9]

TMM

2015

~

~

~

0.76

0.70

0.73

~

~

~

Liang et al. [8]

TIP

2015

0.77

0.68

0.71

0.76

0.68

0.72

~

~

~

Michal et al.[12]

FASText

ICCV

2015

~

~

~

0.84

0.69

0.77

~

~

~

Li et al. [3]

TIP

2014

Segmentation

0.80

0.62

0.70

~

~

~

~

~

~

Zhang et al. [14]

CVPR

2016

~

~

~

0.88

0.78

0.83

~

~

~

He et al. [18]

Text-CNN

TIP

2016

0.91

0.74

0.82

0.93

0.73

0.82

0.87

0.73

0.79

Yao et al. [21]

arXiv

2016

~

~

~

0.889

0.802

0.843

~

~

~

Hu et al. [27]

WordSup

ICCV

2017

~

~

~

0.933

0.875

0.903

~

~

~

Tang et al.[32]

TIP

2017

0.90

0.86

0.88

0.92

0.87

0.89

~

~

~

Wang et al. [38]

ICDAR

2017

0.87

0.78

0.82

0.87

0.82

0.84

~

~

~

Deng et al. [40]

PixelLink

AAAI

2018

~

~

~

0.886

0.875

0.881

~

~

~

Liu et al. [42]

MCN

CVPR

2018

~

~

~

0.88

0.87

0.88

~

~

~

Lyu et al. [43]

CVPR

2018

~

~

~

0.92

0.844

0.880

~

~

~

Chu et al. [45]

Border

ECCV

2018

~

~

~

0.915

0.871

0.892

~

~

~

Wang et al. [54]

PSENet

CVPR

2019

~

~

~

0.94

0.90

0.92

~

~

~

Huang et al. [4]

MSERs-CNN

ECCV

2014

0.88

0.71

0.78

~

~

~

0.84

0.67

0.75

Sun et al. [6]

PR

2015

0.92

0.91

0.91

0.94

0.92

0.93

~

~

~

Gupta et al. [15]

FCRN

CVPR

2016

Regression

0.94

0.77

0.85

0.938

0.764

0.842

~

~

~

Zhong et al. [20]

DeepText

arXiv

2016

0.87

0.83

0.85

0.85

0.81

0.83

~

~

~

Liao et al. [22]

TextBoxes

AAAI

2017

0.89

0.82

0.86

0.89

0.83

0.86

~

~

~

Liu et al. [25]

DMPNet

CVPR

2017

~

~

~

0.93

0.83

0.870

~

~

~

Jiang et al. [36]

R2CNN

arXiv

2017

~

~

~

0.92

0.81

0.86

~

~

~

Xing et al. [37]

ArbiText

arXiv

2017

~

~

~

0.826

0.936

0.877

~

~

~

Wang et al. [41]

ITN

CVPR

2018

0.896

0.889

0.892

0.941

0.893

0.916

~

~

~

Liao et al. [49]

TextBoxes++

TIP

2018

~

~

~

0.92

0.86

0.89

~

~

~

He et al. [50]

TIP

2018

~

~

~

0.91

0.84

0.88

~

~

~

Ma et al. [51]

RRPN

TMM

2018

~

~

~

0.95

0.89

0.91

~

~

~

Zhu et al. [55]

SLPR

arXiv

2018

~

~

~

0.90

0.72

0.80

~

~

~

Cai et al. [59]

FFN

ICIP

2018

~

~

~

0.92

0.84

0.876

~

~

~

Sabyasachi et al. [60]

RGC

ICIP

2018

~

~

~

0.89

0.77

0.83

~

~

~

Wang et al. [82]

CVPR

2019

~

~

~

0.937

0.878

0.907

~

~

~

Liu et al. [84]

CVPR

2019

~

~

~

0.937

0.897

0.917

~

~

~

He et al. [29]

SSTD

ICCV

2017

~

~

~

0.89

0.86

0.88

~

~

~

Tian et al. [11]

ICCV

2015

0.86

0.76

0.81

0.852

0.759

0.802

~

~

~

Tian et al. [13]

CTPN

ECCV

2016

~

~

~

0.93

0.83

0.88

~

~

~

He et al. [19]

ACCV

2016

~

~

~

0.90

0.75

0.81

~

~

~

Shi et al. [23]

SegLink

CVPR

2017

~

~

~

0.877

0.83

0.853

~

~

~

Tian et al. [30]

WeText

ICCV

2017

~

~

~

0.911

0.831

0.869

~

~

~

Zhu et al. [31]

RTN

ICDAR

2017

~

~

~

0.94

0.89

0.91

~

~

~

Ren et al. [34]

TMM

2017

0.78

0.67

0.72

0.81

0.67

0.73

~

~

~

Zhang et al. [10]

CVPR

2015

0.84

0.76

0.80

0.88

0.74

0.80

~

~

~

Tang et al. [52]

SSFT

TMM

2018

Hybrid

0.906

0.847

0.876

0.911

0.861

0.885

~

~

~

Xie et al.[61]

SPCNet

AAAI

2019

~

~

~

0.94

0.91

0.92

~

~

~

Liu et al. [80]

BDN

IJCAI

2019

~

~

~

0.887

0.894

0.89

~

~

~

Zhou et al. [24]

EAST

CVPR

2017

~

~

~

0.93

0.83

0.870

~

~

~

Yue et al. [48]

BMVC

2018

~

~

~

0.885

0.846

0.870

~

~

~

Zhong et al. [53]

AF-RPN

arXiv

2018

~

~

~

0.94

0.90

0.92

~

~

~

2.2.2 Detection Results on Arbitrary-Quadrilateral-Text Datasets

Method

Model

Source

Time

Method Category

IC15 [70]

MSRA-TD500 [71]

USTB-SV1K [65]

SVT [66]

P

R

F

P

R

F

P

R

F

P

R

F

Le et al. [5]

HOCC

CVPR

2014

Traditional

~

~

~

0.71

0.62

0.66

~

~

~

~

~

~

Yin et al. [7]

TPAMI

2015

~

~

~

0.81

0.63

0.71

0.499

0.454

0.475

~

~

~

Wu et al. [9]

TMM

2015

~

~

~

0.63

0.70

0.66

~

~

~

~

~

~

Tian et al. [17]

IJCAI

2016

~

~

~

0.95

0.58

0.721

0.537

0.488

0.51

~

~

~

Yang et al. [33]

TIP

2017

~

~

~

0.95

0.58

0.72

0.54

0.49

0.51

~

~

~

Liang et al. [8]

TIP

2015

~

~

~

0.74

0.66

0.70

~

~

~

~

~

~

Zhang et al. [14]

CVPR

2016

Segmentation

0.71

0.43

0.54

0.83

0.67

0.74

~

~

~

~

~

~

Zhu et al. [16]

CVPR

2016

0.81

0.91

0.85

~

~

~

~

~

~

~

~

~

He et al. [18]

Text-CNN

TIP

2016

~

~

~

0.76

0.61

0.69

~

~

~

~

~

~

Yao et al. [21]

arXiv

2016

0.723

0.587

0.648

0.765

0.753

0.759

~

~

~

~

~

~

Hu et al. [27]

WordSup

ICCV

2017

0.793

0.77

0.782

~

~

~

~

~

~

~

~

~

Wu et al. [28]

ICCV

2017

0.91

0.78

0.84

0.77

0.78

0.77

~

~

~

~

~

~

Dai et al. [35]

FTSN

arXiv

2017

0.886

0.80

0.841

0.876

0.771

0.82

~

~

~

~

~

~

Deng et al. [40]

PixelLink

AAAI

2018

0.855

0.820

0.837

0.830

0.732

0.778

~

~

~

~

~

~

Liu et al. [42]

MCN

CVPR

2018

0.72

0.80

0.76

0.88

0.79

0.83

~

~

~

~

~

~

Lyu et al. [43]

CVPR

2018

0.895

0.797

0.843

0.876

0.762

0.815

~

~

~

~

~

~

Chu et al. [45]

Border

ECCV

2018

~

~

~

0.830

0.774

0.801

~

~

~

~

~

~

Long et al. [46]

TextSnake

ECCV

2018

0.849

0.804

0.826

0.832

0.739

0.783

~

~

~

~

~

~

Yang et al. [47]

IncepText

IJCAI

2018

0.938

0.873

0.905

0.875

0.790

0.830

~

~

~

~

~

~

Wang et al. [54]

PSENet

CVPR

2019

0.8692

0.845

0.8569

~

~

~

~

~

~

~

~

~

Xu et al.[57]

TextField

arXiv

2018

0.843

0.805

0.824

0.874

0.759

0.813

~

~

~

~

~

~

Tian et al. [58]

FTDN

ICIP

2018

0.847

0.773

0.809

~

~

~

~

~

~

~

~

~

Tian et al. [83]

CVPR

2019

0.883

0.850

0.866

0.842

0.817

0.829

~

~

~

~

~

~

Baek et al. [62]

CRAFT

CVPR

2019

0.898

0.843

0.869

0.882

0.782

0.829

~

~

~

~

~

~

Gupta et al. [15]

FCRN

CVPR

2016

Regression

~

~

~

~

~

~

~

~

~

0.651

0.599

0.624

Liu et al. [25]

DMPNet

CVPR

2017

0.732

0.682

0.706

~

~

~

~

~

~

~

~

~

He et al. [26]

DDR

ICCV

2017

0.82

0.80

0.81

0.77

0.70

0.74

~

~

~

~

~

~

Jiang et al. [36]

R2CNN

arXiv

2017

0.856

0.797

0.825

~

~

~

~

~

~

~

~

~

Xing et al. [37]

ArbiText

arXiv

2017

0.792

0.735

0.759

0.78

0.72

0.75

~

~

~

~

~

~

Wang et al. [41]

ITN

CVPR

2018

0.857

0.741

0.795

0.903

0.723

0.803

~

~

~

~

~

~

Liao et al. [44]

RRD

CVPR

2018

0.88

0.8

0.838

0.876

0.73

0.79

~

~

~

~

~

~

Liao et al. [49]

TextBoxes++

TIP

2018

0.878

0.785

0.829

~

~

~

~

~

~

~

~

~

He et al. [50]

TIP

2018

0.85

0.80

0.82

0.91

0.81

0.86

~

~

~

~

~

~

Ma et al. [51]

RRPN

TMM

2018

0.822

0.732

0.774

0.821

0.677

0.742

~

~

~

~

~

~

Zhu et al. [55]

SLPR

arXiv

2018

0.855

0.836

0.845

~

~

~

~

~

~

~

~

~

Deng et al. [56]

arXiv

2018

0.89

0.81

0.845

~

~

~

~

~

~

~

~

~

Sabyasachi et al. [60]

RGC

ICIP

2018

0.83

0.81

0.82

0.85

0.76

0.80

~

~

~

~

~

~

Wang et al. [82]

CVPR

2019

0.892

0.86

0.876

0.852

0.821

0.836

~

~

~

~

~

~

He et al. [29]

SSTD

ICCV

2017

0.80

0.73

0.77

~

~

~

~

~

~

~

~

~

Tian et al. [13]

CTPN

ECCV

2016

0.74

0.52

0.61

~

~

~

~

~

~

~

~

~

He et al. [19]

ACCV

2016

~

~

~

~

~

~

~

~

~

0.87

0.73

0.79

Shi et al. [23]

SegLink

CVPR

2017

0.731

0.768

0.75

0.86

0.70

0.77

~

~

~

~

~

~

Tang et al. [52]

SSFT

TMM

2018

Hybrid

~

~

~

~

~

~

~

~

~

0.541

0.758

0.631

Xie et al.[61]

SPCNet

AAAI

2019

0.89

0.86

0.87

~

~

~

~

~

~

~

~

~

Liu et al. [64]

PMTD

arXiv

2019

0.913

0.874

0.893

~

~

~

~

~

~

~

~

~

Liu et al. [80]

BDN

IJCAI

2019

0.881

0.846

0.863

0.87

0.815

0.842

~

~

~

~

~

~

Zhang et al. [81]

LOMO

CVPR

2019

0.878

0.876

0.877

~

~

~

~

~

~

~

~

~

Zhou et al. [24]

EAST

CVPR

2017

0.833

0.783

0.807

0.873

0.674

0.761

~

~

~

~

~

~

Yue et al. [48]

BMVC

2018

0.866

0.789

0.823

~

~

~

~

~

~

0.691

0.660

0.675

Zhong et al. [53]

AF-RPN

arXiv

2018

0.89

0.83

0.86

~

~

~

~

~

~

~

~

~

Method

Model

Source

Time

Method Category

COCO-Text [72]

RCTW-17 [73]

MLT [76]

OSTD[77]

P

R

F

P

R

F

P

R

F

P

R

F

Le et al. [5]

HOCC

CVPR

2014

Traditional

~

~

~

~

~

~

~

~

~

0.80

0.73

0.76

Yao et al. [21]

arXiv

2016

Segmentation

0.432

0.27

0.333

~

~

~

~

~

~

~

~

~

Hu et al. [27]

WordSup

ICCV

2017

0.452

0.309

0.368

~

~

~

~

~

~

~

~

~

Lyu et al. [43]

CVPR

2018

0.351

0.348

0.349

~

~

~

0.743

0.706

0.724

~

~

~

Chu et al. [45]

Border

ECCV

2018

~

~

~

0.782

0.588

0.671

0.777

0.621

0.690

~

~

~

Yang et al. [47]

IncepText

IJCAI

2018

~

~

~

0.785

0.569

0.660

~

~

~

~

~

~

Wang et al. [54]

PSENet

CVPR

2019

~

~

~

~

~

~

0.7535

0.6918

0.7213

~

~

~

Baek et al. [62]

CRAFT

CVPR

2019

~

~

~

~

~

~

0.806

0.682

0.739

~

~

~

He et al. [29]

SSTD

ICCV

2017

Regression

0.46

0.31

0.37

~

~

~

~

~

~

~

~

~

Gupta et al. [15]

FCRN

CVPR

2016

~

~

~

~

~

~

0.844

0.763

0.801

~

~

~

Liao et al. [49]

TextBoxes++

TIP

2018

0.61

0.57

0.59

~

~

~

~

~

~

~

~

~

Ma et al. [51]

RRPN

TMM

2018

~

~

~

~

~

~

0.7669

0.5794

0.6601

~

~

~

Deng et al. [56]

arXiv

2018

0.555

0.633

0.591

~

~

~

~

~

~

~

~

~

Cai et al. [59]

FFN

ICIP

2018

0.43

0.35

0.39

~

~

~

~

~

~

~

~

~

Xie et al. [79]

DeRPN

AAAI

2019

0.586

0.557

0.571

~

~

~

~

~

~

~

~

~

He et al. [29]

SSTD

ICCV

2017

0.46

0.31

0.37

~

~

~

~

~

~

~

~

~

Xie et al.[61]

SPCNet

AAAI

2019

Hybrid

~

~

~

~

~

~

0.806

0.686

0.741

~

~

~

Liu et al. [64]

PMTD

arXiv

2019

~

~

~

~

~

~

0.844

0.763

0.801

~

~

~

Liu et al. [80]

BDN

IJCAI

2019

~

~

~

~

~

~

0.791

0.698

0.742

~

~

~

Zhang et al. [81]

LOMO

CVPR

2019

~

~

~

0.791

0.602

0.684

0.802

0.672

0.731

~

~

~

Zhou et al. [24]

EAST

CVPR

2017

0.504

0.324

0.395

~

~

~

~

~

~

~

~

~

Zhong et al. [53]

AF-RPN

arXiv

2018

~

~

~

~

~

~

0.75

0.66

0.70

~

~

~

2.2.3 Detection Results on Irregular-Text Datasets

In this section, we only select those methods suitable for irregular text detection.

Method	Model	Source	Time	Method Category	Total-text [74]			SCUT-CTW1500 [75]
					P	R	F	P	R	F
Baek et al. [62]	CRAFT	CVPR	2019	Segmentation	0.876	0.799	0.836	0.860	0.811	0.835
Long et al. [46]	TextSnake	ECCV	2018		0.827	0.745	0.784	0.679	0.853	0.756
Tian et al. [83]		CVPR	2019		~	~	~	81.7	84.2	80.1
Wang et al. [54]	PSENet	CVPR	2019		0.840	0.779	0.809	0.848	0.797	0.822
Zhu et al. [55]	SLPR	arXiv	2018	Regression	~	~	~	0.801	0.701	0.748
Liu et al. [63]	CTD+TLOC	PR	2019		~	~	~	0.774	0.698	0.734
Wang et al. [82]		CVPR	2019		~	~	~	80.1	80.2	80.1
Liu et al. [84]		CVPR	2019		0.814	0.791	0.802	0.787	0.761	0.774
Zhang et al. [81]	LOMO	CVPR	2019	Hybrid	87.6	79.3	83.3	85.7	76.5	80.8
Xie et al.[61]	SPCNet	AAAI	2019		0.83	0.83	0.83	~	~	~

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

[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

[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

4. Evaluation

If you are insterested in developing better scene text detection metrics, some references recommended here might be useful.

[A] Wolf, Christian, and Jean-Michel Jolion. “Object count/area graphs for the evaluation of object detection and segmentation algorithms.” International Journal of Document Analysis and Recognition (IJDAR) 8.4 (2006): 280-296. paper

[B] 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

[C] Calarasanu, Stefania, Jonathan Fabrizio, and Severine Dubuisson. “What is a good evaluation protocol for text localization systems? Concerns, arguments, comparisons and solutions.” Image and Vision Computing 46 (2016): 1-17. paper

[D] Shi, Baoguang, et al. “ICDAR2017 competition on reading chinese text in the wild (RCTW-17).” 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). Vol. 1. IEEE, 2017. paper

[E] Nayef, N; Yin, F; Bizid, I; et al. ICDAR2017 robust reading challenge on multi-lingual scene text detection and script identification-rrc-mlt. In Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on, volume 1, 1454–1459. IEEE.
paper

[F] Dangla, Aliona, et al. “A first step toward a fair comparison of evaluation protocols for text detection algorithms.” 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). IEEE, 2018. paper

[G] He,Mengchao and Liu, Yuliang, et al. ICPR2018 Contest on Robust Reading for Multi-Type Web images. ICPR 2018. paper

[H] Liu, Yuliang and Jin, Lianwen, et al. “Tightness-aware Evaluation Protocol for Scene Text Detection” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019. paper code

5. OCR Service

OCR	API	Free
Tesseract OCR Engine	×	√
Azure	√	√
ABBYY	√	√
OCR Space	√	√
SODA PDF OCR	√	√
Free Online OCR	√	√
Online OCR	√	√
Super Tools	√	√
Online Chinese Recognition	√	√
Calamari OCR	×	√
Tencent OCR	√	×

6. References and Code

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