https://github.com/HCIILAB/Scene-Text-Detection
https://github.com/HCIILAB/Scene-Text-Detection
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- Host: GitHub
- URL: https://github.com/HCIILAB/Scene-Text-Detection
- Owner: HCIILAB
- Created: 2019-05-15T09:25:49.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2023-09-07T05:15:06.000Z (over 2 years ago)
- Last Synced: 2024-11-03T09:33:37.708Z (over 1 year ago)
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- Stars: 540
- Watchers: 32
- Forks: 129
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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- awesome-deep-text-detection-recognition - HCIILAB-Detection
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README
# Scene Text Detection Resources
Author: Chongyu Liu
# Updates
Dec 4, 2020: Add 2 papers from CVPR2020/ECCV2020 and update corresponding tables.
------
- [1.Datasets](#1-datasets)
- [1.1 Horizontal-Text Datasets](#11-Horizontal-Text-Datasets)
- [1.2 Arbitrary-Quadrilateral-Text Datasets](#12-Arbitrary-Quadrilateral-Text-Datasets)
- [1.3 Irregular-Text Datasets](#13-Irregular-Text-Datasets)
- [1.4 Synthetic Datasets](#14-synthetic-datasets)
- [1.5 Comparison of Datasets](#15-comparison-of-datasets)
- [2. Summary of Scene Text Detection Resources](#2-summary-of-scene-text-detection-results)
- [2.1 Comparison of Methods](#21-comparison-of-methods)
- [2.1.1 Traditional Methods](#211-traditional-methods)
- [2.1.2 Segmentation-based Methods](#212-Pixel-level-methods-methods)
- [2.1.3 Regression-based Methods](#213-regression-methods)
- [2.1.4 Hybrid Methods](#214-hybrid-methods)
- [2.2 Detection Results](#22-detection-result)
- [2.2.1 Detection Results on Horizontal-Text Datasets](#221-Detection-Results-on-Horizontal-Text-Datasets)
- [2.2.2 Detection Results on Arbitrary-Quadrilateral-Text Datasets](#222-Detection-Results-on-Arbitrary-Quadrilateral-Text-Datasets)
- [2.2.3 Detection Results on Irregular-Text Datasets](#223-Detection-Results-on-Irregular-Text-Datasets)
- [3. Survey](#3-survey)
- [4. Evaluation](#4-Evaluation)
- [5. OCR Service](#5-ocr-service)
- [6. References and Code](#6-references)
------
### 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](http://www.iapr-tc11.org/mediawiki/index.php?title=ICDAR_2003_Robust_Reading_Competitions)
- 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](http://www.cvc.uab.es/icdar2011competition/?com=downloads)
- 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](http://dagdata.cvc.uab.es/icdar2013competition/?ch=2&com=downloads)
### 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](http://prir.ustb.edu.cn/TexStar/MOMV-text-detection/)
- 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](http://vision.ucsd.edu/~kai/grocr/)
- 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](https://pan.baidu.com/s/1rhYUn1mIo8OZQEGUZ9Nmrg ) \(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](http://rrc.cvc.uab.es/?ch=4&com=downloads)
- 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](https://vision.cornell.edu/se3/coco-text-2/)
- 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](http://pages.ucsd.edu/~ztu/Download_front.htm)
- 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](http://rrc.cvc.uab.es/?ch=8)
- 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](http://rrc.cvc.uab.es/?ch=15)
- 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](https://ctwdataset.github.io/)
- 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](http://rctw.vlrlab.net/dataset/)
- 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](http://rrc.cvc.uab.es/?ch=12)
### 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](http://cs-chan.com/downloads_CUTE80_dataset.html)
- 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](https://github.com/cs-chan/Total-Text-Dataset)
- 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](https://github.com/Yuliang-Liu/Curve-Text-Detector)
- 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](https://rrc.cvc.uab.es/?ch=16)
- 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](https://rrc.cvc.uab.es/?ch=14)
* 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](http://www.robots.ox.ac.uk/~vgg/data/scenetext/)
* 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](https://github.com/ankush-me/SynthText)
### 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.
Wang et al.[92]
ContourNet
✓
✓
✓
✓
CVPR
2020
1.A scale-insensitive Adaptive Region Proposal Network (AdaptiveRPN); 2. Local Orthogonal Texture-aware Module (LOTM).
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.
Xiao et al. [93]
SDM
✕
✓
✓
✓
ECCV
2020
1. A novel sequential deformation method; 2. auxiliary character counting supervision.
#### 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
~
~