/Scene-Text-Detection

Scene Text Detection Resources

Author: Chongyu Liu

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

  • ArTs：

    • 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;

(c) 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.
Richardson et al. [87]
✕ ✓ ✓ ✕ WACV 2019 Presenting an additional scale predictor the estimate the better scale of text regions for testing.
Wang et al. [88] SAST ✕ ✓ ✓ ✓ ACMM 2019 Presenting a context attended multi-task learning framework for scene text detection.
Wang et al. [90] PAN ✕ ✓ ✓ ✓ ICCV 2019 Proposing an efficient and accurate arbitrary-shaped text detector called Pixel Aggregation Network(PAN),

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.
Wang et al. [86] DSRN ✕ ✓ ✓ ✕ IJCAI 2019 Presenting a scale-transfer module and scale relationship module to handle the problem of scale variation.
Tang et al.[89] Seglink++ ✕ ✓ ✓ ✓ PR 2019 Presenting instance aware component grouping (ICG) for arbitrary-shape text detection.

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.
Xue et al.[85] MSR ✕ ✓ ✓ ✓ IJCAI 2019 Presenting a noval multi-scale regression network.
Liao et al. [91] DB ✓ ✓ ✓ ✓ AAAI 2020 Presenting differentiable binarization module to adaptively set the thresholds for binarization, which simplifies the post-processing.

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 ~ ~ ~
Xue et al.[85] MSR IJCAI 2019 ~ ~ ~ 0.918 0.885 0.901 ~ ~ ~

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 ~ ~ ~ ~ ~ ~
Richardson et al. [87]
IJCAI 2019 0.853 0.83 0.827 ~ ~ ~ ~ ~ ~ ~ ~ ~
Wang et al. [88] SAST ACMM 2019 0.8755 0.8734 0.8744 ~ ~ ~ ~ ~ ~ ~ ~ ~
Wang et al. [90] PAN ICCV 2019 0.84 0.819 0.829 0.844 0.838 0.821 ~ ~ ~ ~ ~ ~
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 ~ ~ ~ ~ ~ ~
Wang et al. [86] DSRN IJCAI 2019 0.832 0.796 0.814 0.876 0.712 0.785 ~ ~ ~ ~ ~ ~
Tang et al.[89] Seglink++ PR 2019 0.837 0.803 0.820 ~ ~ ~ ~ ~ ~ ~ ~ ~
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 ~ ~ ~ ~ ~ ~ ~ ~ ~
Xue et al.[85] MSR IJCAI 2019 ~ ~ ~ 0.874 0.767 0.817 ~ ~ ~ ~ ~ ~
Liao et al. [91] DB AAAI 2020 0.918 0.832 0.873 0.915 0.792 0.849 ~ ~ ~ ~ ~ ~
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 ~ ~ ~ ~ ~ ~ ~ ~ ~ 0.80 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 ~ ~ ~ ~ ~ ~ ~ ~ ~
Liao et al. [44] RRD CVPR 2018 ~ ~ ~ 0.591 0.775 0.670 ~ ~ ~ ~ ~ ~
Richardson et al. [87]
IJCAI 2019 ~ ~ ~ ~ ~ ~ 0.729 0.618 0.669 ~ ~ ~
Wang et al. [88] SAST ACMM 2019 ~ ~ ~ ~ ~ ~ 0.7935 0.6653 0.7237 ~ ~ ~
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 ~ ~ ~
Liao et al. [91] DB AAAI 2020 ~ ~ ~ ~ ~ ~ 0.831 0.679 0.747 ~ ~ ~

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
Wang et al. [88] SAST ACMM 2019 0.8557 0.7549 0.802 0.8119 0.8171 0.8145
Wang et al. [90] PAN ICCV 2019 0.893 0.81 0.85 0.864 0.812 0.837
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
Tang et al.[89] Seglink++ PR 2019 0.829 0.809 0.815 0.828 0.798 0.813
Zhang et al. [81] LOMO CVPR 2019 Hybrid 0.876 0.793 0.833 0.857 0.765 0.808
Xie et al.[61] SPCNet AAAI 2019 0.83 0.83 0.83 ~ ~ ~
Xue et al.[85] MSR IJCAI 2019 0.852 0.73 0.768 0.838 0.778 0.807
Liao et al. [91] DB AAAI 2020 0.871 0.825 0.847 0.869 0.802 0.834

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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                                                                                   Datasets
USTB-SV1K[65]：Xu-Cheng Yin, Xuwang Yin, Kaizhu Huang, and Hong-Wei Hao, Robust text detection in natural scene images, IEEE Trans. Pattern Analysis and Machine Intelligence (TPAMI), priprint, 2013. Paper
SVT[66]: Wang,Kai, and S. Belongie. Word Spotting in the Wild. European Conference on Computer Vision(ECCV), 2010: 591-604. Paper
ICDAR2005[67]: Lucas, S: ICDAR 2005 text locating competition results. In: ICDAR ,2005. Paper
ICDAR2011[68]: Shahab, A, Shafait, F, Dengel, A: ICDAR 2011 robust reading competition challenge 2: Reading text in scene images. In: ICDAR, 2011. Paper
ICDAR2013[69]：D. Karatzas, F. Shafait, S. Uchida, et al. ICDAR 2013 robust reading competition. In ICDAR, 2013. Paper
ICDAR2015[70]：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
MSRA-TD500[71]：C. Yao, X. Bai, W. Liu, Y. Ma, and Z. Tu, Detecting texts of arbitrary orientations in natural images. in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2012, pp.1083–1090.Paper
COCO-Text[72]：Veit A, Matera T, Neumann L, et al. Coco-text: Dataset and benchmark for text detection and recognition in natural images. arXiv preprint arXiv:1601.07140, 2016. Paper
RCTW-17[73]：Shi B, Yao C, Liao M, et al. ICDAR2017 competition on reading chinese text in the wild (RCTW-17). Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on. IEEE, 2017, 1: 1429-1434. Paper
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