Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/HCIILAB/Scene-Text-Recognition

Last synced: 3 months ago
JSON representation

Host: GitHub
URL: https://github.com/HCIILAB/Scene-Text-Recognition
Owner: HCIILAB
Created: 2019-05-15T08:38:44.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2021-12-17T09:05:40.000Z (about 3 years ago)
Last Synced: 2024-08-01T04:02:07.580Z (5 months ago)
Size: 1.05 MB
Stars: 603
Watchers: 32
Forks: 117
Open Issues: 2
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

awesomeai - Scene Text Recognition Resources
awesome-ai-awesomeness - Scene Text Recognition Resources
awesome-ai-awesomeness - Scene Text Recognition Resources

README

# Scene Text Recognition Resources
[

Author: 陈晓雪

](https://github.com/CCchenxiaoxue)
The paper "Text Recognition in the Wild: A Survey" (accepted to appear in ACM Computing Surveys) in [arXiv](https://arxiv.org/pdf/2005.03492v3.pdf) version is available now.

# ❗❗ Newest Version Can be Found Here ❗❗
## This repository is no longer maintaining now, and you can refer to our newest one
## [Scene Text Recognition Recommendations](https://github.com/HCIILAB/Scene-Text-Recognition-Recommendations)

## Updates

Dec 24, 2019: add 20 papers and update corresponding tables.

Feb 29, 2020: add AAAI-2020 papers and update corresponding tables.

May 8, 2020: add CVPR-2020 papers and update corresponding tables.

Dec 8, 2020: add 11 papers and update corresponding tables. You can download the new [Excel](https://pan.baidu.com/s/1xitxu7R5hw27pVV7eJ1c7w) prepared by us. (Password: sj2t)

***

- [1. Datasets](#1-datasets)
- [1.1 Regular Latin Datasets](#11-regular-latin-datasets)
- [1.2 Irregular Latin Datasets](#12-irregular-latin-datasets)
- [1.3 Multilingual Datasets](#13-multilingual-datasets)
- [1.4 Synthetic Datasets](#14-synthetic-datasets)
- [1.5 Comparison of the Benchmark Datasets](#15-comparison-of-the-benchmark-datasets)
- [2. Performance Comparison of Recognition Algorithms](#2-performance-comparison-of-recognition-algorithms)
- [2.1 Characteristics Comparison of Recognition Approaches](#21-characteristics-comparison-of-recognition-approaches)
- [2.2 Performance Comparison on Benchmark Datasets](#22-performance-comparison-on-benchmark-datasets)
- [2.2.1 Performance Comparison of Recognition Algorithms on Regular Latin Datasets](#221-performance-comparison-of-recognition-algorithms-on-regular-latin-datasets)
- [2.2.2 Performance Comparison of Recognition Algorithms on Irregular Latin Datasets](#222-performance-comparison-of-recognition-algorithms-on-irregular-latin-datasets)
- [3. Survey](#3-survey)
- [4. OCR Service](#4-ocr-service)
- [5. References](#5-references)
- [6.Help](#6help)
- [7.Copyright](#7copyright)

## 1. Datasets

### 1.1 Regular Latin Datasets

- IIIT5K[31]：
* **Introduction:** The IIIT5K dataset [31] contains 5,000 text instance images: 2,000 for training and 3,000 for testing. It contains words from street scenes and from originally-digital images. Every image is associated with a 50 -word lexicon and a 1,000 -word lexicon. Specifically, the lexicon consists of a ground-truth word and some randomly picked words.
* **Link:** [IIIT5K-download](http://cvit.iiit.ac.in/research/projects/cvit-projects/the-iiit-5k-word-dataset)
- SVT[1]：
* **Introduction:** The SVT dataset [1] contains 350 images: 100 for training and 250 for testing. Some images are severely corrupted by noise, blur, and low resolution. Each image is associated with a 50 -word lexicon.
* **Link:** [SVT-download](http://vision.ucsd.edu/~kai/svt/)
- ICDAR 2003(IC03)[33]：
* **Introduction:** The IC03 dataset [33] contains 509 images: 258 for training and 251 for testing. Specifically, it contains 867 cropped text instances after discarding images that contain non-alphanumeric characters or less than three characters. Every image is associated with a 50 -word lexicon and a full-word lexicon. Moreover, the full lexicon combines all lexicon words.
* **Link:** [IC03-download](http://www.iapr-tc11.org/mediawiki/index.php?title=ICDAR_2003_Robust_Reading_Competitions)
- ICDAR 2013(IC13)[34]：
* **Introduction:** The IC13 dataset [34] contains 561 images: 420 for training and 141 for testing. It inherits data from the IC03 dataset and extends it with new images. Similar to IC03 dataset, the IC13 dataset contains 1,015 cropped text instance images after removing the words with non-alphanumeric characters. No lexicon is associated with IC13 . Notably, 215 duplicate text instance images [65] exist between the IC03 training dataset and the IC13 testing dataset. Therefore, care should be taken regarding the overlapping data when evaluating a model on the IC13 testing data.
* **Link:** [IC13-download](http://dagdata.cvc.uab.es/icdar2013competition/?ch=2&com=downloads)
- SVHN[45]：
* **Introduction:** The SVHN [45] dataset contains more than 600,000 digits of house numbers in natural scenes. It is obtained from a large number of street view images using a combination of automated algorithms and the Amazon Mechanical Turk (AMT) framework. The SVHN dataset was typically used for scene digit recognition.
* **Link:** [SVHN-download](http://ufldl.stanford.edu/housenumbers/)

### 1.2 Irregular Latin Datasets

- SVT-P[35]：
- **Introduction:** The SVT-P [35] dataset contains 238 images with 639 cropped text instances. It is specifically designed to evaluate perspective distorted text recognition. It is built based on the original SVT dataset by selecting the images at the same address on Google Street View but with different view angles. Therefore, most text instances are heavily distorted by the non-frontal view angle. Moreover, each image is associated with a 50-word lexicon and a full-word lexicon.
- **Link:** [SVT-P-download](https://pan.baidu.com/s/1rhYUn1mIo8OZQEGUZ9Nmrg ) \(Password : vnis)
- CUTE80[36]：
- **Introduction:** The CUTE80 dataset [36] contains 80 high-resolution images with 288 cropped text
instances. It focuses on curved text recognition. Most images in CUTE80 have a complex background, perspective distortion, and poor resolution. No lexicon is associated with CUTE80.
- **Link:** [CUTE80-download](http://cs-chan.com/downloads_CUTE80_dataset.html)
- ICDAR 2015(IC15)[37]：
- **Introduction:** The IC15 dataset [37] contains 1,500 images: 1,000 for training and 500 for testing.
Specifically, it contains 2,077 cropped text instances, including more than 200 irregular text samples. As text images were taken by Google Glasses without ensuring the image quality, most of the text is very small, blurred, and multi-oriented. No lexicon is provided.
- **Link:** [IC15-download](http://rrc.cvc.uab.es/?ch=4&com=downloads)
- COCO-Text[38]：
- **Introduction:** The COCO-Text dataset [38] contains 63,686 images with 145,859 cropped text instances. It is the first large-scale dataset for text in natural images and also the first dataset to annotate scene text with attributes such as legibility and type of text. However, no lexicon is associated with COCO-Text.
- **Link:** [COCO-Text-download](https://vision.cornell.edu/se3/coco-text-2/)
- Total-Text[39]：
- **Introduction:** The Total-Text dataset [39] contains 1,555 images with 11,459 cropped text instance images. It focuses on curved scene text recognition. Images in Total-Text have more than three different orientations, including horizontal, multi-oriented, and curved. No lexicon is associated with Total-Text.
- **Link:** [Total-Text-download](https://github.com/cs-chan/Total-Text-Dataset)

### 1.3 Multilingual Datasets

- RCTW-17(RCTW competition，ICDAR17)[40]：
- **Introduction:** The RCTW-17 dataset contains 12,514 images: 11,514 for training and 1,000 for testing. Most are natural images collected by cameras or mobile phones, whereas others are digital-born. Text instances are annotated with labels, fonts, languages, etc.
- **Link:** [RCTW-17-download](http://rctw.vlrlab.net/dataset/)
- MTWI(competition)[41]：
- **Introduction:** The MTWI dataset contains 20,000 images. This is the first dataset constructed by Chinese and Latin web text. Most images in MTWI have a relatively high resolution and cover diverse types of web text, including multi-oriented text, tightly-stacked text, and complex-shaped text.
- **Link:** [MTWI-download ](https://pan.baidu.com/s/1SUODaOzV7YOPkrun0xSz6A#list/path=%2F) \(Password:gox9)
- CTW[42]：
- **Introduction:** The CTW dataset includes 32,285 high-resolution street view images with 1,018,402 character instances. All images have character-level annotations: the underlying character, the bounding box, and six other attributes.
- **Link:** [CTW-download](https://ctwdataset.github.io/)
- SCUT-CTW1500[43]：
- **Introduction:** The SCUT-CTW1500 dataset contains 1,500 images: 1,000 for training and 500
for testing. In particular, it provides 10,751 cropped text instance images, including 3,530 with curved text. The images are manually harvested from the Internet, image libraries such as Google Open-Image, or phone cameras. The dataset contains a lot of horizontal and multi-oriented text
- **Link:** [SCUT-CTW1500-download](https://github.com/Yuliang-Liu/Curve-Text-Detector)
* LSVT(LSVT competition, ICDAR2019)[57]:
* **Introduction:** The LSVT dataset contains 20,000 testing samples, 30,000 fully annotated training samples, and 400,000 training samples with weak annotations (i.e., with partial labels). All images are captured from streets and reflect a large variety of complicated real-world scenarios, e.g., store fronts and landmarks.
* **Link:** [LSVT-download](https://rrc.cvc.uab.es/?ch=16&com=downloads)
* ArT(ArT competition, ICDAR2019)[58]:
* **Introduction:** The ArT dataset [58] contains 10,166 images: 5,603 for training and 4,563 for testing. ArT is a combination of Total-Text, SCUT-CTW 1500 , and Baidu Curved Scene Text 4 , which was collected to introduce the arbitrary-shaped text problem. Moreover, all existing text shapes (i.e., horizontal, multi-oriented, and curved) have multiple occurrences in the ArT dataset.
* **Link:** [ArT-download](https://rrc.cvc.uab.es/?ch=16&com=downloads)
* ReCTS-25k(ReCTS competition, ICDAR2019)[59]:
* **Introduction:** The ReCTS-25k dataset [59] contains 25,000 images: 20,000 for training and 5,000 for testing. All the images are from the Meituan-Dianping Group, collected by Meituan business mer-
chants, using phone cameras under uncontrolled conditions. Specifically, ReCTS-25 k dataset mainly contains images of Chinese text on signboards.
* **Link:** [ReCTS-download](https://rrc.cvc.uab.es/?ch=16&com=downloads)
* MLT(MLTcompetition, ICDAR2019) [81]:
* **Introduction:** The MLT-2019 dataset [81] contains 20,000 images: 10,000 for training (1,000 per language) and 10,000 for testing. The dataset includes ten languages, representing seven different scripts: Arabic, Bangla, Chinese, Devanagari, English, French, German, Italian, Japanese, and Korean. The number of images per script is equal.
* **Link:** [MLT-download](https://rrc.cvc.uab.es/?ch=15&com=downloads)

### 1.4 Synthetic Datasets

* Synth90k [53] :
* **Introduction:** The Synth90k dataset contains 9 million synthetic text instance images from a set of 90k common English words. Words are rendered onto natural images with random transformations and effects, such as random fonts, colors, blur, and noises. Synth90k dataset can emulate the distribution of scene text images and can be used instead of real-world data to train data-hungry deep learning algorithms. Besides, every image is annotated with a ground-truth word.
* **Link:** [Synth90k-download](http://www.robots.ox.ac.uk/~vgg/data/text/)
* SynthText [54] :
* **Introduction:** The SynthText dataset contains 800,000 images with 6 million synthetic text instances. As in the generation of Synth90k dataset, the text sample is rendered using a randomly selected font and transformed according to the local surface orientation. Moreover, each image is annotated with a ground-truth word.
* **Link:** [SynthText-download](https://github.com/ankush-me/SynthText)
* Verisimilar Synthesis [73] :
* **Introduction:** The Verisimilar Synthesis dataset [73] contains 5 million synthetic text instance images. Given background images and source texts, a semantic map and a saliency map are first
determined which are then combined to identify semantically sensible and apt locations for text embedding. The color, brightness, and orientation of the source texts are further determined adaptively according to the color, brightness, and contextual structures around the embedding locations within the background image.
* **Link:** [Verisimilar Synthesis](https://github.com/fnzhan/Verisimilar-Image-Synthesis-for-Accurate-Detection-and-Recognition-of-Texts-in-Scenes)
* UnrealText [88]:
* **Introduction:** The UnrealText dataset [88] contains 600K synthetic images with 12 million cropped text instances. It is developed upon Unreal Engine 4 and the UnrealCV plugin [89]. Text instances are regarded as planar polygon meshes with text foregrounds loaded as texture. These meshes are placed in suitable positions in 3D world, and rendered together with the scene as a whole. The same font set from [Google Fonts](https://fonts.google.com/) and the same text corpus, i.e., Newsgroup20, are used as SynthText does.
* **Link:** [Verisimilar Synthesis](https://github.com/fnzhan/Verisimilar-Image-Synthesis-for-Accurate-Detection-and-Recognition-of-Texts-in-Scenes)

### 1.5 Comparison of the Benchmark Datasets

Comparison of the Benchmark Datasets

Datasets
Language
Images
Lexicon
Label
Type

Pictures
Training Pictures
Testing Pictures
Instances
Training Instances
Testing Instances
50
1k
Full
None
Char
Word

IIIT5K[31]
English
1120
380
740
5000
2000
3000
√
√
×
√
√
√
Regular

SVT[32]
English
350
100
250
725
211
514
√
×
×
√
×
√
Regular

IC03[33]
English
509
258
251
2268
1157
1111
√
√
√
√
√
√
Regular

IC13[34]
English
561
420
141
5003
3564
1439
×
×
×
√
√
√
Regular

SVHN[45]
Digits
600000
573968
26032
600000
573968
26032
×
×
×
√
√
√
Regular

SVT-P[35]
English
238
0
238
639
0
639
√
×
√
√
×
√
Irregular

CUTE80[36]
English
80
0
80
288
0
288
×
×
×
√
×
√
Irregular

IC15[37]
English
1500
1000
500
6545
4468
2077
×
×
×
√
×
√
Irregular

COCO-Text[38]
English
63686
43686
10000
145859
118309
27550
×
×
×
√
×
√
Irregular

Total-Text[39]
English
1555
1255
300
11459
11166
293
×
×
×
√
×
√
Irregular

RCTW-17[40]
Chinese/English
12514
11514
1000
-
-
-
×
×
×
√
×
√
Regular

MTWI[41]
Chinese/English
20000
10000
10000
290206
141476
148730
×
×
×
√
×
√
Regular

CTW[42]
Chinese/English
32285
25887
3269
1018402
812872
103519
×
×
×
√
√
√
Regular

SCUT-CTW1500[43]
Chinese/English
1500
1000
500
10751
7683
3068
×
×
×
√
×
√
Irregular

LSVT[57], [63]
Chinese/English
450000
30000
20000
-
-
-
×
×
×
√
×
√
Irregular

ArT[58]
Chinese/English
10166
5603
4563
98455
50029
48426
×
×
×
√
×
√
Irregular

ReCTS-25k[59]
Chinese/English
25000
20000
5000
119713
108924
10789
×
×
×
√
√
√
Irregular

MLT[81]
Multilingual
20000
10000
10000
191639
89177
102462
×
×
×
√
×
√
Irregular

Synth90k[53]
English
~9000000
-
-
~9000000
-
-
×
×
×
√
×
√
Regular

SynthText[54]
English
~6000000
-
-
~6000000
-
-
×
×
×
√
√
√
Regular

Verisimilar Synthesis[73]
English
-
-
-
~5000000
-
-
×
×
×
√
×
√
Regular

UnrealText[88]
English
~600000
-
-
~12000000
-
-
×
×
×
√
√
√
Regular

***

## 2. Performance Comparison of Recognition Algorithms

### 2.1 Characteristics Comparison of Recognition Approaches

It is notable that 1) "Reg" stands for regular Latin datasets. 2) "Irreg" stands for irregular Latin datasets. 3) "Seg" denotes the segmentation-based methods. 4) "Extra" indicates the methods that use the extra datasets other than Synth90k and SynthText. 5) "CTC" represents the methods that apply the CTC-based algorithm to decode. 6) "Attn" represents the method that apply the attention mechanism to decode.

You can also download the new [Excel](https://pan.baidu.com/s/1xitxu7R5hw27pVV7eJ1c7w) prepared by us. (Password: sj2t)

Characteristics Comparison of Recognition Approaches

Method
Code
Regular
Irregular
Segmentation
Extra data
CTC
Attention
Source
Time
Highlight

Wang et al. [1] : ABBYY
√
√
×
√
×
×
×
ICCV
2011
a state-of-the-art text detector + a leading commercial OCR engine

Wang et al. [1] : SYNTH+PLEX
√
√
×
×
×
×
×
ICCV
2011
the baseline of scene text recognition

Mishra et al. [2]
×
√
×
√
×
×
×
BMVC
2012
1) incorporating higher order statistical language models to recognize words in an unconstrained manner 2) introducing IIIT5K-word dataset

Wang et al. [3]
√
√
×
√
×
×
×
ICPR
2012
CNNs + Non-maximal suppression + beam search

Goel et al. [4] : wDTW
×
√
×
√
×
×
×
ICDAR
2013
recognizing text by matching the scene and synthetic image features with wDTW

Bissacco et al. [5] : PhotoOCR
×
√
×
√
×
×
×
ICCV
2013
applying a network with five hidden layers for character classification

Phan et al. [6]
×
×
√
√
×
×
×
ICCV
2013
1) MSER + SIFT descriptors + SVM 2) introducing the SVT-P datasets

Alsharif et al. [7] : HMM/Maxout
×
√
×
√
×
×
×
ICLR
2014
convolutional Maxout networks + Hybrid HMM

Almazan et al [8] : KCSR
√
√
×
×
×
×
×
TPAMI
2014
embedding word images and text strings in a common vectorial subspace and interpreting the task of recognition and retrieval as a nearest neighbor problem

Yao et al. [9] : Strokelets
×
√
×
√
×
×
×
CVPR
2014
proposing a novel multi-scale representation for scene text recognition: strokelets

R.-Serrano et al.[10] : Label embedding
×
√
×
×
×
×
×
IJCV
2015
embedding word labels and word images into a common Euclidean space and finding the cloest word label in this space

Jaderberg et al. [11]
√
√
×
√
×
×
×
ECCV
2014
1) enabling efficient feature sharing for text detection and classification 2) making technical changes over the traditional CNN architectures 3) proposing a method of automated data mining of Flickr.

Su and Lu [12]
×
√
×
×
×
√
×
ACCV
2014
HOG + BLSTM + CTC

Gordo[13] : Mid-features
×
√
×
√
×
×
×
CVPR
2015
proposing local mid-level features for building word image representations

Jaderberg et al. [14]
√
√
×
×
×
×
×
IJCV
2015
1) treating each word as a category and training very large convolutional neural networks to perform word recognition on the whole proposal region 2) generating 9 million images with equal numbers of word samples from a 90k word dictionary

Jaderberg et al. [15]
×
√
×
×
×
×
×
ICLR
2015
CNN + CRF

Shi, Bai, and Yao [16] : CRNN
√
√
×
×
×
√
×
TPAMI
2017
CNN + BLSTM + CTC

Shi et al. [17] : RARE
×
×
√
×
×
×
√
CVPR
2016
STN + CNN + attentional BLSTM

Lee and Osindero [18] : R2AM
×
√
×
×
×
×
√
CVPR
2016
presenting recursive recurrent neural networks with attention modeling

Liu et al. [19] : STAR-Net
×
×
√
×
×
√
×
BMVC
2016
STN + ResNet + BLSTM + CTC

Liu et al. [78]
×
√
×
√
√
×
×
ICPR
2016
integrating the CNN and WFST classification model

Mishra et al. [77]
×
√
×
√
√
×
×
CVIU
2016
character detection (HOG/CNN + SVM +Sliding window) + CRF, combining bottom-up cues from character detection and top-down cues from lexicon

Su and Lu [76]
×
√
×
×
√
√
×
PR
2017
HOG(different scale) + BLSTM + CTC (ensemble)

*Yang et al. [20]
×
×
√
×
√
×
√
IJCAI
2017
1) CNN + 2D attention-based RNN, applying an auxiliary dense character detection task that helps to learn text specific visual patterns 2) developing a large-scale synthetic dataset

Yin et al. [21]
×
√
×
×
×
√
×
ICCV
2017
CNN + CTC

Wang et al.[66] : GRCNN
√
√
×
×
×
√
×
NIPS
2017
Gated Recurrent Convulution Layer + BLSTM + CTC

*Cheng et al. [22] : FAN
×
√
×
×
√
×
√
ICCV
2017
1) proposing the concept of attention drift 2)introducing focusing network to focus deviated attention back on the target areas

Cheng et al. [23] : AON
×
×
√
×
×
×
√
CVPR
2018
1) extracting scene text features in four directions 2) CNN + Attentional BLSTM

Gao et al. [24]
×
√
×
×
×
√
√
NC
2019
attentional ResNet + CNN + CTC

Liu et al. [25] : Char-Net
×
×
√
√
×
×
√
AAAI
2018
CNN + STN (facilitating the rectification of individual characters) + LSTM

*Liu et al. [26] : SqueezedText
×
√
×
×
√
×
×
AAAI
2018
binary convolutional encoder-decoder network + Bi-RNN

Zhan et al.[73]
√
√
×
×
√
√
×
CVPR
2018
CRNN, achieving verisimilar scene text image synthesis by combining three novel designs, including semantic coherence, visual attention, and adaptive text appearance

*Bai et al. [27] : EP
×
√
×
×
√
×
√
CVPR
2018
proposing edit probability to effectively handle the misalignment between the training text and the output probability distribution sequence

Fang et al.[74]
√
√
×
×
×
×
√
MultiMedia
2018
ResNet + [2D Attentional CNN, CNN-based language module]

Liu et al.[75] : EnEsCTC
√
√
×
×
×
√
×
NIPS
2018
proposing a novel maximum entropy based regularization for CTC (EnCTC) and an entropy-based pruning method (EsCTC) to effectively reduce the space of the feasible set

Liu et al. [28]
×
√
×
×
×
√
×
ECCV
2018
designing a multi-task network with an encoder-discriminator-generator architecture to guide the feature of the original image toward that of the clean image

Wang et al.[61] : MAAN
×
√
×
×
×
×
√
ICFHR
2018
ResNet + BLSTM + Memory-Augmented attentional decoder

Gao et al. [29]
×
√
×
×
×
√
√
ICIP
2018
attentional DenseNet + BLSTM + CTC

Shi et al. [30] : ASTER
√
×
√
×
×
×
√
TPAMI
2018
TPS + ResNet + bidirectional attention-based BLSTM

Chen et al. [60] : ASTER + AEG
×
×
√
×
×
×
√
NC
2019
TPS + ResNet + bidirectional attention-based BLSTM + AEG

Luo et al. [46] : MORAN
√
×
√
×
×
×
√
PR
2019
Multi-object rectification network + CNN + attentional BLSTM

Luo et al. [61] : MORAN-v2
√
×
√
×
×
×
√
PR
2019
Multi-object rectification network + ResNet + attentional BLSTM

Chen et al. [60] : MORAN-v2 + AEG
×
×
√
×
×
×
√
NC
2019
Multi-object rectification network + ResNet + attentional BLSTM + AEG

Xie et al. [47] : CAN
×
√
×
×
×
×
√
ACM
2019
ResNet + CNN + GLU

*Liao et al.[48] : CA-FCN
×
×
√
√
√
×
√
AAAI
2019
performing character classification at each pixel location and needing character-level annotations

*Li et al. [49] : SAR
√
×
√
×
√
×
√
AAAI
2019
ResNet + 2D attentional LSTM

Zhan el at. [55]: ESIR
×
×
√
×
×
×
√
CVPR
2019
Iterative rectification Network + ResNet + attentional BLSTM

Zhang et al. [56]: SSDAN
×
√
×
√
×
×
√
CVPR
2019
attentional CNN + GAS + GRU

Yang et al. [62]: ScRN
×
×
√
×
√
×
√
ICCV
2019
Symmetry-constrained Rectification Network + ResNet + BLSTM + attentional GRU

Wang et al. [64]: GCAM
×
√
×
×
×
×
√
ICME
2019
Convolutional Block Attention Module (CBAM) + ResNet + BLSTM + the proposed Gated Cascade Attention Module (GCAM)

Jeonghun et al. [65]
√
×
√
×
×
×
√
ICCV
2019
TPS + ResNet + BLSTM + Attention Mechanism

Huang et al. [67] : EPAN
×
×
√
×
×
×
√
NC
2019
learning to sample features from the text region of 2D feature maps and innovatively introducing a two-stage attention mechanism

Gao et al. [68]
×
√
×
×
×
√
×
NC
2019
attentional DenseNET + 4-layer CNN + CTC

Qi et al. [69] : CCL
×
√
×
×
√
√
×
ICDAR
2019
ResNet + [CTC, CCL]

Wang et al. [70] : ReELFA
×
×
√
×
√
×
√
ICDAR
2019
VGG + attentional LSTM, utilizing one-hot encoded coordinates to indicate the spatial relationship of pixels and character center masks to help focus attention on the right feature areas

Zhu et al. [71] : HATN
×
×
√
×
√
×
√
ICIP
2019
ResNet50 + Hierarchical Attention Mechanism (Transformer structure)

Zhan et al. [72] : SF-GAN
×
√
×
×
√
×
√
CVPR
2019
ResNet50 + attentional Decoder, synthesising realistic scene text images for training better recognition models

Liao et al. [79] : SAM
√
×
√
×
×
×
√
TPAMI
2019
Spatial attentional module (SAM)

Liao et al. [79] : seg-SAM
√
×
√
×
√
×
√
TPAMI
2019
Character segmentation module + Spatial attention module (SAM)

Wang et al. [80] : DAN
√
×
√
×
×
×
√
AAAI
2020
decoupling the decoder of the traditional attention mechanism into a convolutional alignment module and a decoupled text decoder

Wang et al. [82] : TextSR
√
×
√
×
×
×
√
arXiv
2019
attempting to solve small texts with super-resolution methods

Wan et al. [83] : TextScanner
×
×
√
√
√
×
×
AAAI
2020
an effective segmentation-based dual-branch framework for scene text recognition

Hu et al. [84] : GTC
×
×
√
×
√
√
√
AAAI
2020
attempting to use GCN to learn the local correlations of feature sequence

Luo et al. [85]
×
×
√
×
×
×
√
IJCV
2020
separating text content from noisy background styles

*Litman et al. [86]
×
×
√
×
√
×
√
CVPR
2020
training a deep BiLSTM encoder, thus improving the encoding of contextual dependencies

Yu et al. [87]
×
×
√
×
×
×
√
CVPR
2020
introducing a global semantic reasoning module (GSRM) to capture global semantic context through multi-way parallel transmission

Qiao et al. [101] : SEED
√
×
√
×
×
×
√
CVPR
2020
proposing a semantics enhanced encoder-decoder framework to robustly recognize low-quality scene texts

Bleeker et al. [93] : Bi-STET
√
√
×
×
×
×
√
ECAI
2020
a novel bidirectional STR method with a single decoder for bidirectional text decoding

*Bartz et al. [94] : KISS
√
×
√
×
√
×
√
arXiv
2020
a new model for STR that consists of two ResNet based feature extractors, a spatial transformer, and a transformer

Zhang et al. [95] : SPIN
×
×
√
×
×
×
√
arXiv
2020
a new learnable geometric-unrelated module which allows the color manipulation of source data within the network

Lin et al. [96] : FASDA
×
√
×
×
×
×
√
arXiv
2020
implementing sequence-level domain adaption for STR

Zhang et al. [98] : AutoSTR
√
×
√
×
×
×
√
ECCV
2020
searching data-dependent backbones

Mou et al. [99] : PlugNet
×
×
√
×
×
×
√
ECCV
2020
combining the pluggable super-resolution unit to solve the low-quality text recognition from the feature-level

*Yue et al. [100] : RobustScanner
×
×
√
×
√
×
√
ECCV
2020
mitigating the misrecognition problem of the encoderdecoder with attention framework on contextless text images

### 2.2 Performance Comparison on Benchmark Datasets

In this section, we compare the performance of the current advanced algorithms on benchmark datasets, including IIIT5K，SVT，IC03，IC13，SVT-P，CUTE80，IC15，COCO-Text, RCTW-17, MWTI, CTW，SCUT-CTW1500, LSVT, ArT and ReCTS-25k.

It is notable that 1) The '*' indicates the methods that use the extra datasets other than Synth90k and SynthText. 2) The **bold** represents the best recognition results. 3) '^' denotes the best recognition results of using extra datasets. 4) '@' represents the methods under different evaluation that only uses 1811 test images. 5) 'SK', 'ST', 'ExPu', 'ExPr' and 'Un' indicates the methods that use Synth90K, SynthText, Extra Public Data, Extra Private Data and unknown data, respectively. 6) 'D_A' means data augmentation. 7) IC5-S contains only 1811 cropped text instances.

#### 2.2.1 Performance Comparison of Recognition Algorithms on Regular Latin Datasets

Performance Comparison of Recognition Algorithms on Regular Latin Datasets

Method
IIIT5K
SVT
IC03
IC13
Data
Source
Time

50
1K
None
50
None
50
Full
50k
None
None

Wang et al. [1] : ABBYY
24.3
-
-
35
-
56
55
-
-
-
Un
ICCV
2011

Wang et al. [1] : SYNTH+PLEX
-
-
-
57
-
76
62
-
-
-
ExPr
ICCV
2011

Mishra et al. [2]
64.1
57.5
-
73.2
-
81.8
67.8
-
-
-
ExPu
BMVC
2012

Wang et al. [3]
-
-
-
70
-
90
84
-
-
-
ExPr
ICPR
2012

Goel et al. [4] : wDTW
-
-
-
77.3
-
89.7
-
-
-
-
Un
ICDAR
2013

Bissacco et al. [5] : PhotoOCR
-
-
-
90.4
78
-
-
-
-
87.6
ExPr
ICCV
2013

Phan et al. [6]
-
-
-
73.7
-
82.2
-
-
-
-
ExPu
ICCV
2013

Alsharif et al. [7] : HMM/Maxout
-
-
-
74.3
-
93.1
88.6
85.1
-
-
ExPu
ICLR
2014

Almazan et al [8] : KCSR
88.6
75.6
-
87
-
-
-
-
-
-
ExPu
TPAMI
2014

Yao et al. [9] : Strokelets
80.2
69.3
-
75.9
-
88.5
80.3
-
-
-
ExPu
CVPR
2014

R.-Serrano et al.[10] : Label embedding
76.1
57.4
-
70
-
-
-
-
-
-
ExPu
IJCV
2015

Jaderberg et al. [11]
-
-
-
86.1
-
96.2
91.5
-
-
-
ExPu
ECCV
2014

Su and Lu [12]
-
-
-
83
-
92
82
-
-
-
ExPu
ACCV
2014

Gordo[13] : Mid-features
93.3
86.6
-
91.8
-
-
-
-
-
-
ExPu
CVPR
2015

Jaderberg et al. [14]
97.1
92.7
-
95.4
80.7
98.7
98.6
93.3
93.1
90.8
ExPr
IJCV
2015

Jaderberg et al. [15]
95.5
89.6
-
93.2
71.7
97.8
97
93.4
89.6
81.8
SK + ExPr
ICLR
2015

Shi, Bai, and Yao [16] : CRNN
97.8
95
81.2
97.5
82.7
98.7
98
95.7
91.9
89.6
SK
TPAMI
2017

Shi et al. [17] : RARE
96.2
93.8
81.9
95.5
81.9
98.3
96.2
94.8
90.1
88.6
SK
CVPR
2016

Lee and Osindero [18] : R2AM
96.8
94.4
78.4
96.3
80.7
97.9
97
-
88.7
90
SK
CVPR
2016

Liu et al. [19] : STAR-Net
97.7
94.5
83.3
95.5
83.6
96.9
95.3
-
89.9
89.1
SK + ExPr
BMVC
2016

*Liu et al. [78]
94.1
84.7
-
92.5
-
96.8
92.2
-
-
-
ExPu (D_A)
ICPR
2016

*Mishra et al. [77]
78.07
-
46.73
78.2
-
88
-
-
67.7
60.18
ExPu (D_A)
CVIU
2016

*Su and Lu [76]
-
-
-
91
-
95
89
-
-
76
SK + ExPu
PR
2017

*Yang et al. [20]
97.8
96.1
-
95.2
-
97.7
-
-
-
-
ExPu
IJCAI
2017

Yin et al. [21]
98.7
96.1
78.2
95.1
72.5
97.6
96.5
-
81.1
81.4
SK
ICCV
2017

Wang et al.[66] : GRCNN
98
95.6
80.8
96.3
81.5
98.8
97.8
-
91.2
-
SK
NIPS
2017

*Cheng et al. [22] : FAN
99.3
97.5
87.4
97.1
85.9
99.2
97.3
-
94.2
93.3
SK + ST (Pixel_wise)
ICCV
2017

Cheng et al. [23] : AON
99.6
98.1
87
96
82.8
98.5
97.1
-
91.5
-
SK + ST (D_A)
CVPR
2018

Gao et al. [24]
99.1
97.9
81.8
97.4
82.7
98.7
96.7
-
89.2
88
SK
NC
2019

Liu et al. [25] : Char-Net
-
-
83.6
-
84.4
-
93.3
-
91.5
90.8
SK (D_A)
AAAI
2018

*Liu et al. [26] : SqueezedText
97
94.1
87
95.2
-
98.8
97.9
93.8
93.1
92.9
ExPr
AAAI
2018

*Zhan et al.[73]
98.1
95.3
79.3
96.7
81.5
-
-
-
-
87.1
Pr(5 million)
CVPR
2018

*Bai et al. [27] : EP
99.5
97.9
88.3
96.6
87.5
98.7
97.9
-
94.6
94.4
SK + ST (Pixel_wise)
CVPR
2018

Fang et al.[74]
98.5
96.8
86.7
97.8
86.7
99.3
98.4
-
94.8
93.5
SK + ST
MultiMedia
2018

Liu et al.[75] : EnEsCTC
-
-
82
-
80.6
-
-
-
92
90.6
SK
NIPS
2018

Liu et al. [28]
97.3
96.1
89.4
96.8
87.1
98.1
97.5
-
94.7
94
SK
ECCV
2018

Wang et al.[61] : MAAN
98.3
96.4
84.1
96.4
83.5
97.4
96.4
-
92.2
91.1
SK
ICFHR
2018

Gao et al. [29]
99.1
97.2
83.6
97.7
83.9
98.6
96.6
-
91.4
89.5
SK
ICIP
2018

Shi et al. [30] : ASTER
99.6
98.8
93.4
97.4
89.5
98.8
98
-
94.5
91.8
SK + ST
TPAMI
2018

Chen et al. [60] : ASTER + AEG
99.5
98.5
94.4
97.4
90.3
99
98.3
-
95.2
95
SK + ST
NC
2019

Luo et al. [46] : MORAN
97.9
96.2
91.2
96.6
88.3
98.7
97.8
-
95
92.4
SK + ST
PR
2019

Luo et al. [61] : MORAN-v2
-
-
93.4
-
88.3
-
-
-
94.2
93.2
SK + ST
PR
2019

Chen et al. [60] : MORAN-v2 + AEG
99.5
98.7
94.6
97.4
90.4
98.8
98.3
-
95.3
95.3
SK + ST
NC
2019

Xie et al. [47] : CAN
97
94.2
80.5
96.9
83.4
98.4
97.8
-
91
90.5
SK
ACM
2019

*Liao et al.[48] : CA-FCN
^99.8
98.9
92
98.8
82.1
-
-
-
-
91.4
SK + ST+ ExPr
AAAI
2019

*Li et al. [49] : SAR
99.4
98.2
95
98.5
91.2
-
-
-
-
94
SK + ST + ExPr
AAAI
2019

Zhan el at. [55]: ESIR
99.6
98.8
93.3
97.4
90.2
-
-
-
-
91.3
SK + ST
CVPR
2019

Zhang et al. [56]: SSDAN
-
-
83.8
-
84.5
-
-
-
92.1
91.8
SK
CVPR
2019

*Yang et al. [62]: ScRN
99.5
98.8
94.4
97.2
88.9
99
98.3
-
95
93.9
SK + ST(char-level + word-level)
ICCV
2019

Wang et al. [64]: GCAM
-
-
93.9
-
91.3
-
-
-
95.3
95.7
SK + ST
ICME
2019

Jeonghun et al. [65]
-
-
87.9
-
87.5
-
-
-
94.4
92.3
SK + ST
ICCV
2019

Huang et al. [67]：EPAN
98.9
97.8
94
96.6
88.9
98.7
98
-
95
94.5
SK + ST
NC
2019

Gao et al. [68]
99.1
97.9
81.8
97.4
82.7
98.7
96.7
-
89.2
88
SK
NC
2019

*Qi et al. [69] : CCL
99.6
99.1
91.1
98
85.9
99.2
^98.8
-
93.5
92.8
SK + ST(char-level + word-level)
ICDAR
2019

*Wang et al. [70] : ReELFA
99.2
98.1
90.9
-
82.7
-
-
-
-
-
ST(char-level + word-level)
ICDAR
2019

*Zhu et al. [71] : HATN
-
-
88.6
-
82.2
-
-
-
91.3
91.1
SK(D_A) + Pu
ICIP
2019

*Zhan et al. [72] : SF-GAN
-
-
63
-
69.3
-
-
-
-
61.8
Pr(1 million)
CVPR
2019

Liao et al. [79] : SAM
99.4
98.6
93.9
98.6
90.6
98.8
98
-
95.2
95.3
SK + ST
TPAMI
2019

*Liao et al. [79] : seg-SAM
^99.8
^99.3
95.3
99.1
91.8
99
97.9
-
95
95.3
SK + ST (char-level)
TPAMI
2019

Wang et al. [80] : DAN
-
-
94.3
-
89.2
-
-
-
95
93.9
SK + ST
AAAI
2020

Wang et al. [82] : TextSR
-
-
92.5
98
87.2
-
-
-
93.2
91.3
SK + ST
arXiv
2019

*Wan et al. [83] : TextScanner
99.7
99.1
93.9
98.5
90.1
-
-
-
-
92.9
SK + ST (char-level)
AAAI
2020

*Hu et al. [84] : GTC
-
-
^95.8
-
^92.9
-
-
-
95.5
94.4
SK + ST + ExPu
AAAI
2020

Luo et al. [85]
99.6
98.8
95.6
99.4
92.9
99.1
98.8
-
96.2
96
SK + ST
IJCV
2020

*Litman et al. [86]
-
-
93.7
-
92.7
-
-
-
^96.3
93.9
SK + ST + ExPu
CVPR
2020

Yu et al. [87]
-
-
94.8
-
91.5
-
-
-
-
95.5
SK + ST
CVPR
2020

Qiao et al. [101] : SEED
-
-
93.8
-
89.6
-
-
-
-
92.8
SK + ST
CVPR
2020

Bleeker et al. [93] : Bi-STET
99.6
98.9
94.7
97.4
89
99.1
98.7
-
96
93.4
SK + ST
ECAI
2020

*Bartz et al. [94] : KISS
-
-
94.6
-
89.2
-
-
-
-
93.1
SK + ST + ExPu (D_A)
arXiv
2020

Zhang et al. [95] : SPIN
-
-
94.7
-
90.3
-
-
-
94.4
92.8
SK + ST
arXiv
2020

Lin et al. [96] : FASDA
-
-
-
96.5
88.3
99.1
97.5
-
94.8
94.4
SK
arXiv
2020

Zhang et al. [98] : AutoSTR
-
-
94.7
-
90.9
-
-
-
93.3
94.2
SK + ST
ECCV
2020

Mou et al. [99] : PlugNet
-
-
94.4
-
92.3
-
-
-
95.7
95
SK + ST
ECCV
2020

*Yue et al. [100] : RobustScanner
-
-
95.4
-
89.3
-
-
-
-
94.1
SK + ST + ExPu
ECCV
2020

#### 2.2.2 Performance Comparison of Recognition Algorithms on Irregular Latin Datasets

Performance Comparison of Recognition Algorithms on Irregular Latin Datasets

Method
SVT-P
CUTE80
IC15-S
IC15
COCO-TEXT
Data
Source
Time

50
Full
None
None
None
None
None

Wang et al. [1] : ABBYY
40.5
26.1
-
-
-
-
-
Un
ICCV
2011

Wang et al. [1] : SYNTH+PLEX
-
-
-
-
-
-
-
ExPr
ICCV
2011

Mishra et al. [2]
45.7
24.7
-
-
-
-
-
ExPu
BMVC
2012

Wang et al. [3]
40.2
32.4
-
-
-
-
-
ExPr
ICPR
2012

Goel et al. [4] : wDTW
-
-
-
-
-
-
-
Un
ICDAR
2013

Bissacco et al. [5] : PhotoOCR
-
-
-
-
-
-
-
ExPr
ICCV
2013

Phan et al. [6]
62.3
42.2
-
-
-
-
-
ExPu
ICCV
2013

Alsharif et al. [7] : HMM/Maxout
-
-
-
-
-
-
-
ExPu
ICLR
2014

Almazan et al [8] : KCSR
-
-
-
-
-
-
-
ExPu
TPAMI
2014

Yao et al. [9] : Strokelets
-
-
-
-
-
-
-
ExPu
CVPR
2014

R.-Serrano et al.[10] : Label embedding
-
-
-
-
-
-
-
ExPu
IJCV
2015

Jaderberg et al. [11]
-
-
-
-
-
-
-
ExPu
ECCV
2014

Su and Lu [12]
-
-
-
-
-
-
-
ExPu
ACCV
2014

Gordo[13] : Mid-features
-
-
-
-
-
-
-
ExPu
CVPR
2015

Jaderberg et al. [14]
-
-
-
-
-
-
-
ExPr
IJCV
2015

Jaderberg et al. [15]
-
-
-
-
-
-
-
SK + ExPr
ICLR
2015

Shi, Bai, and Yao [16] : CRNN
-
-
-
-
-
-
-
SK
TPAMI
2017

Shi et al. [17] : RARE
91.2
77.4
71.8
59.2
-
-
-
SK
CVPR
2016

Lee and Osindero [18] : R2AM
-
-
-
-
-
-
-
SK
CVPR
2016

Liu et al. [19] : STAR-Net
94.3
83.6
73.5
-
-
-
-
SK + ExPr
BMVC
2016

*Liu et al. [78]
-
-
-
-
-
-
-
ExPu (D_A)
ICPR
2016

*Mishra et al. [77]
-
-
-
-
-
-
-
ExPu (D_A)
CVIU
2016

*Su and Lu [76]
-
-
-
-
-
-
-
SK + ExPu
PR
2017

*Yang et al. [20]
93
80.2
75.8
69.3
-
-
-
ExPu
IJCAI
2017

Yin et al. [21]
-
-
-
-
-
-
-
SK
ICCV
2017

Wang et al.[66] : GRCNN
-
-
-
-
-
-
-
SK
NIPS
2017

*Cheng et al. [22] : FAN
-
-
-
-
70.6
-
-
SK + ST (Pixel_wise)
ICCV
2017

Cheng et al. [23] : AON
94
83.7
73
76.8
-
68.2
-
SK + ST (D_A)
CVPR
2018

Gao et al. [24]
-
-
-
-
-
-
-
SK
NC
2019

Liu et al. [25] : Char-Net
-
-
73.5
-
-
60
-
SK (D_A)
AAAI
2018

*Liu et al. [26] : SqueezedText
-
-
-
-
-
-
-
ExPr
AAAI
2018

*Zhan et al.[73]
-
-
-
-
-
-
-
Pr(5 million)
CVPR
2018

*Bai et al. [27] : EP
-
-
-
-
73.9
-
-
SK + ST (Pixel_wise)
CVPR
2018

Fang et al.[74]
-
-
-
-
-
71.2
-
SK + ST
MultiMedia
2018

Liu et al.[75] : EnEsCTC
-
-
-
-
-
-
-
SK
NIPS
2018

Liu et al. [28]
-
-
73.9
62.5

-
-
SK
ECCV
2018

Wang et al.[61] : MAAN
-
-
-
-
-
-
-
SK
ICFHR
2018

Gao et al. [29]
-
-
-
-
-
-
-
SK
ICIP
2018

Shi et al. [30] : ASTER
-
-
78.5
79.5
76.1
-
-
SK + ST
TPAMI
2018

Chen et al. [60] : ASTER + AEG
94.4
89.5
82
80.9
-
76.7
-
SK + ST
NC
2019

Luo et al. [46] : MORAN
94.3
86.7
76.1
77.4
-
68.8
-
SK + ST
PR
2019

Luo et al. [61] : MORAN-v2
-
-
79.7
81.9
-
73.9
-
SK + ST
PR
2019

Chen et al. [60] : MORAN-v2 + AEG
94.7
89.6
82.8
81.3
-
77.4
-
SK + ST
NC
2019

Xie et al. [47] : CAN
-
-
-
-
-
-
-
SK
ACM
2019

*Liao et al.[48] : CA-FCN
-
-
-
78.1
-
-
-
SK + ST+ ExPr
AAAI
2019

*Li et al. [49] : SAR
^95.8
91.2
^86.4
89.6
-
78.8
^66.8
SK + ST + ExPr
AAAI
2019

Zhan el at. [55]: ESIR
-
-
79.6
83.3
-
76.9
-
SK + ST
CVPR
2019

Zhang et al. [56]: SSDAN
-
-
-
-
-
-
-
SK
CVPR
2019

*Yang et al. [62]: ScRN
-
-
80.8
87.5
-
78.7
-
SK + ST(char-level + word-level)
ICCV
2019

Wang et al. [64]: GCAM
-
-
85.7
83.3
83.5
-
-
SK + ST
ICME
2019

Jeonghun et al. [65]
-
-
79.2
74
-
71.8
-
SK + ST
ICCV
2019

Huang et al. [67]：EPAN
91.2
86.4
79.4
82.6
-
73.9
-
SK + ST
NC
2019

Gao et al. [68]
-
-
-
-
-
62.3
40
SK
NC
2019

*Qi et al. [69] : CCL
-
-
-
-
72.9
-
-
SK + ST(char-level + word-level)
ICDAR
2019

*Wang et al. [70] : ReELFA
-
-
-
82.3
-
68.5
-
ST(char-level + word-level)
ICDAR
2019

*Zhu et al. [71] : HATN
-
-
73.5
75.7
-
70.1
-
SK(D_A) + Pu
ICIP
2019

*Zhan et al. [72] : SF-GAN
-
-
48.6
40.6
-
39
-
Pr(1 million)
CVPR
2019

Liao et al. [79] : SAM
-
-
82.2
87.8
-
77.3
-
SK + ST
TPAMI
2019

*Liao et al. [79] : seg-SAM
-
-
83.6
88.5
-
78.2
-
SK + ST (char-level)
TPAMI
2019

Wang et al. [80] : DAN
-
-
80
84.4
-
74.5
-
SK + ST
AAAI
2020

Wang et al. [82] : TextSR
-
-
77.4
78.9
-
75.6
-
SK + ST
arXiv
2019

*Wan et al. [83] : TextScanner
-
-
84.3
83.3
-
79.4
-
SK + ST (char-level)
AAAI
2020

*Hu et al. [84] : GTC
-
-
85.7
92.2
-
79.5
-
SK + ST + ExPu
AAAI
2020

Luo et al. [85]
95.8
91.5
85.1
91.3
83.9
81.4
-
SK + ST
IJCV
2020

*Litman et al. [86]
-
-
^86.9
87.5
-
82.2
-
SK + ST + ExPu
CVPR
2020

Yu et al. [87]
-
-
85.1
87.8
82.7
-
-
SK + ST
CVPR
2020

Qiao et al. [101] : SEED
-
-
81.4
83.6
80
-
-
SK + ST
CVPR
2020

Bleeker et al. [93] : Bi-STET
-
-
80.6
82.5
75.7
-
-
SK + ST
ECAI
2020

*Bartz et al. [94] : KISS
-
-
83.1
89.6
80.3
74.2
-
SK + ST + ExPu (D_A)
arXiv
2020

Zhang et al. [95] : SPIN
-
-
82.8
87.5
82.2
78.5
-
SK + ST
arXiv
2020

Lin et al. [96] : FASDA
-
-
-
-
73.3
-
-
SK
arXiv
2020

Zhang et al. [98] : AutoSTR
-
-
81.7
-
81.8
-
-
SK + ST
ECCV
2020

Mou et al. [99] : PlugNet
-
-
84.3
85
-
82.2
-
SK + ST
ECCV
2020

*Yue et al. [100] : RobustScanner
-
-
82.9
^92.4
-
79.2
-
SK + ST + ExPu
ECCV
2020

***

## 3. Survey

**[50] \[TPAMI-2015]** Q. Ye and D. Doermann, “Text detection and recognition in imagery: A survey,” IEEE Trans. Pattern Anal. Mach. Intell, vol. 37, no. 7, pp. 1480–1500, 2015. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6945320)

**[51] \[Frontiers-Comput. Sci-2016]** Y. Zhu, C. Yao, and X. Bai, “Scene text detection and recognition: Recent advances and future trends,” Frontiers of Computer Science, vol. 10, no. 1, pp. 19–36, 2016. [paper](https://link.springer.com/article/10.1007/s11704-015-4488-0)

**[52] \[IJCV-2020]** Long S, He X, Yao C. Scene text detection and recognition: The deep learning era[J]. International Journal of Computer Vision, 2020: 1-24. [paper](https://link.springer.com/article/10.1007/s11263-020-01369-0) [code](https://github.com/Jyouhou/SceneTextPapers)

**[90]** **\[ACM Computing Surveys-2020]** X. Chen, L. Jin, Y. Zhu, C. Luo, and T. Wang, “Text Recognition in the Wild: A Survey," ACM Computing Surveys (CSUR) 2020. [paper](https://arxiv.org/pdf/2005.03492.pdf) [code](https://github.com/HCIILAB/Scene-Text-Recognition)

***

## 4. OCR Service

| OCR | API | Free | Code |
| :----------------------------------------------------------: | :--: | :--: | :--: |
| [Tesseract OCR Engine](https://github.com/tesseract-ocr/tesseract) | × | √ | √ |
| [Azure](https://azure.microsoft.com/zh-cn/services/cognitive-services/computer-vision/#Analysis) | √ | √ | × |
| [ABBYY](https://www.abbyy.cn/real-time-recognition-sdk/technical-specifications/) | √ | √ | × |
| [OCR Space](https://ocr.space/) | √ | √ | × |
| [SODA PDF OCR](https://www.sodapdf.com/ocr-pdf/) | √ | √ | × |
| [Free Online OCR](https://www.newocr.com/) | √ | √ | × |
| [Online OCR](https://www.onlineocr.net/) | √ | √ | × |
| [Super Tools](https://www.wdku.net/) | √ | √ | × |
| [Online Chinese Recognition](http://chongdata.com/ocr/) | √ | √ | × |
| [Calamari OCR](https://github.com/Calamari-OCR/calamari) | × | √ | √ |
| [ Tencent OCR](https://cloud.tencent.com/product/ocr?lang=cn) | √ | × | × |

***

## 5. References

**[1] \[ICCV-2011]** K. Wang, B. Babenko, and S. Belongie, “End-to-end scene text recognition,” in Proceedings of ICCV, 2011, pp. 1457–1464. [paper ](https://www.researchgate.net/profile/Serge_Belongie/publication/221110077_End-to-end_scene_text_recognition/links/09e4150b34908d2ebb000000/End-to-end-scene-text-recognition.pdf)

**[2] \[BMVC-2012]** A. Mishra, K. Alahari, and C. Jawahar, “Scene text recognition using higher order language priors,” in Proceedings of BMVC, 2012, pp. 1–11. [paper](https://hal.inria.fr/hal-00818183/document) [dataset](http://cvit.iiit.ac.in/research/projects/cvit-projects/the-iiit-5k-word-dataset)

**[3] \[ICPR-2012]** T. Wang, D. J. Wu, A. Coates, and A. Y. Ng, “End-to-end text recognition with convolutional neural networks,” in Proceedings of ICPR, 2012, pp. 3304–3308. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460871&tag=1)

**[4] \[ICDAR-2013]** V. Goel, A. Mishra, K. Alahari, and C. Jawahar, “Whole is greater than sum of parts: Recognizing scene text words,” in Proceedings of ICDAR, 2013, pp. 398–402. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6628652)

**[5] \[ICCV-2013]** A. Bissacco, M. Cummins, Y. Netzer, and H. Neven, “Photoocr: Reading text in uncontrolled conditions,” in Proceedings of ICCV, 2013, pp. 785–792. [paper](http://openaccess.thecvf.com/content_iccv_2013/papers/Bissacco_PhotoOCR_Reading_Text_2013_ICCV_paper.pdf)

**[6] \[ICCV-2013]** T. Quy Phan, P. Shivakumara, S. Tian, and C. Lim Tan, “Recognizing text with perspective distortion in natural scenes,” in Proceedings of ICCV, 2013, pp. 569–576. [paper](http://openaccess.thecvf.com/content_iccv_2013/papers/Phan_Recognizing_Text_with_2013_ICCV_paper.pdf)

**[7] \[ICLR-2014]** O. Alsharif and J. Pineau, “End-to-end text recognition with hybrid HMM maxout models,” in Proceedings of ICLR: Workshop, 2014. [paper](https://arxiv.org/pdf/1310.1811.pdf)

**[8] \[TPAMI-2014]** J. Almaz ́an, A. Gordo, A. Forn ́ es, and E. Valveny, "Word spotting and recognition with embedded attributes,” IEEE Trans. Pattern Anal. Mach. Intell, vol. 36, no. 12, pp. 2552–2566, 2014. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6857995) [code](https://github.com/almazan/watts)

**[9] \[CVPR-2014]** C. Yao, X. Bai, B. Shi, and W. Liu, “Strokelets: A learned multiscale representation for scene text recognition,” in Proceedings of CVPR, 2014, pp. 4042–4049. [paper](http://openaccess.thecvf.com/content_cvpr_2014/papers/Yao_Strokelets_A_Learned_2014_CVPR_paper.pdf)

**[10] \[IJCV-2015]** J. A. Rodriguez-Serrano, A. Gordo, and F. Perronnin, “Label embedding: A frugal baseline for text recognition,” Int. J. Comput. Vis, vol. 113, no. 3, pp. 193–207, 2015. [paper](https://link.springer.com/content/pdf/10.1007%2Fs11263-014-0793-6.pdf)

**[11] \[ECCV-2014]** M. Jaderberg, A. Vedaldi, and A. Zisserman, “Deep features for text spotting,” in Proceedings of ECCV, 2014, pp. 512–528. [paper](https://link.springer.com/content/pdf/10.1007%2F978-3-319-10593-2_34.pdf) [code](https://bitbucket.org/jaderberg/eccv2014_textspotting/src/master/)

**[12] \[ACCV-2014]** B. Su and S. Lu, “Accurate scene text recognition based on recurrent neural network,” in Proceedings of ACCV, 2014, pp. 35–48. [paper](https://link.springer.com/content/pdf/10.1007%2F978-3-319-16865-4_3.pdf)

**[13] \[CVPR-2015]** A. Gordo, “Supervised mid-level features for word image representation,” in Proceedings of CVPR, 2015, pp. 2956–2964. [paper](http://openaccess.thecvf.com/content_cvpr_2015/papers/Gordo_Supervised_Mid-Level_Features_2015_CVPR_paper.pdf)

**[14] \[IJCV-2015]** M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman, “Reading text in the wild with convolutional neural networks,” Int. J. Comput. Vis, vol. 116, no. 1, pp. 1–20, 2016. [paper](https://link.springer.com/content/pdf/10.1007%2Fs11263-015-0823-z.pdf) [code](http://www.robots.ox.ac.uk/~vgg/research/text/)

**[15] \[ICLR-2015]** M. Jaderberg, K. Simonyan, and A. Zisserman, “Deep structured output learning for unconstrained text recognition,” in Proceedings of ICLR, 2015. [paper](https://arxiv.org/pdf/1412.5903.pdf)

**[16] \[TPAMI-2017]** B. Shi, X. Bai, and C. Yao, “An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition,” IEEE Trans. Pattern Anal. Mach. Intell, vol. 39, no. 11, pp. 2298–2304, 2017. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7801919) [code-Torch7](https://github.com/bgshih/crnn) [code-Pytorch](https://github.com/meijieru/crnn.pytorch)

**[17] \[CVPR-2016]** B. Shi, X. Wang, P. Lyu, C. Yao, and X. Bai, “Robust scene text recognition with automatic rectification,” in Proceedings of CVPR, 2016, pp. 4168–4176. [paper](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shi_Robust_Scene_Text_CVPR_2016_paper.pdf)

**[18] \[CVPR-2016]** C.-Y. Lee and S. Osindero, “Recursive recurrent nets with attention modeling for OCR in the wild,” in Proceedings of CVPR, 2016, pp. 2231–2239. [paper](http://openaccess.thecvf.com/content_cvpr_2016/papers/Lee_Recursive_Recurrent_Nets_CVPR_2016_paper.pdf)

**[19] \[BMVC-2016]** W. Liu, C. Chen, K.-Y. K. Wong, Z. Su, and J. Han, “STAR-Net: A spatial attention residue network for scene text recognition,” in Proceedings of BMVC, 2016, p. 7. [paper](https://i.cs.hku.hk/~kykwong/publications/wliu_bmvc16.pdf)

**[20] \[IJCAI-2017]** X. Yang, D. He, Z. Zhou, D. Kifer, and C. L. Giles, “Learning to read irregular text with attention mechanisms,” in Proceedings of IJCAI, 2017, pp. 3280–3286. [paper](https://pdfs.semanticscholar.org/1259/f7533abe2fe85fd9dead92853e2ff07a8792.pdf)

**[21] \[ICCV-2017]** F. Yin, Y.-C. Wu, X.-Y. Zhang, and C.-L. Liu, “Scene text recognition with sliding convolutional character models,” in Proceedings of ICCV, 2017. [paper](https://arxiv.org/pdf/1709.01727.pdf) [code](https://github.com/lsvih/Sliding-Convolution)

**[22] \[ICCV-2017]** Z. Cheng, F. Bai, Y. Xu, G. Zheng, S. Pu, and S. Zhou, “Focusing attention: Towards accurate text recognition in natural images,” in Proceedings of ICCV, 2017, pp. 5086–5094. [paper](http://openaccess.thecvf.com/content_ICCV_2017/papers/Cheng_Focusing_Attention_Towards_ICCV_2017_paper.pdf)

**[23] \[CVPR-2018]** Z. Cheng, Y. Xu, F. Bai, Y. Niu, S. Pu, and S. Zhou, “AON: Towards arbitrarily-oriented text recognition,” in Proceedings of CVPR, 2018, pp. 5571–5579. [paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Cheng_AON_Towards_Arbitrarily-Oriented_CVPR_2018_paper.pdf) [code](https://github.com/huizhang0110/AON)

**[24] \[NC-2019]** Y. Gao, Y. Chen, J. Wang, M. Tang, and H. Lu, “Reading scene text with fully convolutional sequence modeling,” Neurocomputing, vol. 339, pp. 161–170, 2019. [paper](https://www.sciencedirect.com/science/article/pii/S0925231219301870)

**[25] \[AAAI-2018]** W. Liu, C. Chen, and K.-Y. K. Wong, “Char-Net: A character-aware neural network for distorted scene text recognition.” in Proceedings of AAAI, 2018, pp. 7154–7161. [paper](https://i.cs.hku.hk/~kykwong/publications/wliu_aaai2018.pdf)

**[26] \[AAAI-2018]** Z. Liu, Y. Li, F. Ren, W. L. Goh, and H. Yu, “Squeezedtext: A real-time scene text recognition by binary convolutional encoderdecoder network,” in Proceedings of AAAI, 2018, pp. 7194–7201. [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16354/16312)

**[27] \[CVPR-2018]** F. Bai, Z. Cheng, Y. Niu, S. Pu, and S. Zhou, “Edit probability for scene text recognition,” in Proceedings of CVPR, 2018, pp. 1508–1516. [paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Bai_Edit_Probability_for_CVPR_2018_paper.pdf)

**[28] \[ECCV-2018]** Y. Liu, Z. Wang, H. Jin, and I. Wassell, “Synthetically supervised feature learning for scene text recognition,” in Proceedings of ECCV, 2018, pp. 449–465. [paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Yang_Liu_Synthetically_Supervised_Feature_ECCV_2018_paper.pdf)

**[29] \[ICIP-2018]** Y. Gao, Y. Chen, J. Wang, M. Tang, and H. Lu, “Dense chained attention network for scene text recognition,” in Proceedings of ICIP, 2018, pp. 679–683. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8451273)

**[30] \[TPAMI-2018]** B. Shi, M. Yang, X. Wang, P. Lyu, C. Yao, and X. Bai, “ASTER: An attentional scene text recognizer with flexible rectification,” IEEE Trans. Pattern Anal. Mach. Intell, vol. 41, no. 9, pp. 2035–2048, 2019. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8395027) [code](https://github.com/bgshih/aster)

**[31] \[CVPR-2012]** A. Mishra, K. Alahari, and C. Jawahar, “Top-down and bottom-up cues for scene text recognition,” in Proceedings of CVPR, 2012, pp. 2687–2694. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6247990)

**[32]** https://github.com/Canjie-Luo/MORAN_v2

**[33] \[ICDAR-2003]** S. M. Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong, and R. Young, “ICDAR 2003 robust reading competitions,” in Proceedings of ICDAR, 2003, pp. 682–687. [paper](https://link.springer.com/content/pdf/10.1007%2Fs10032-004-0134-3.pdf)

**[34] \[ICDAR-2013]** D. Karatzas, F. Shafait, S. Uchida, M. Iwamura, L. G. i Bigorda, S. R. Mestre, J. Mas, D. F. Mota, J. A. Almazan, and L. P. De Las Heras, “ICDAR 2013 robust reading competition,” in Proceedings of ICDAR, 2013, pp. 1484–1493. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6628859)

**[35] \[ICCV-2013]** T. Quy Phan, P. Shivakumara, S. Tian, and C. Lim Tan, “Recognizing text with perspective distortion in natural scenes,” in Proceedings of ICCV, 2013, pp. 569–576. [paper](http://openaccess.thecvf.com/content_iccv_2013/papers/Phan_Recognizing_Text_with_2013_ICCV_paper.pdf)

**[36] \[Expert Syst.Appl-2014]** A. Risnumawan, P. Shivakumara, C. S. Chan, and C. L. Tan, “A robust arbitrary text detection system for natural scene images,” Expert Systems with Applications, vol. 41, no. 18, pp. 8027–8048, 2014. [paper](https://pdf.sciencedirectassets.com/271506/1-s2.0-S0957417414X00114/1-s2.0-S0957417414004060/main.pdf?x-amz-security-token=AgoJb3JpZ2luX2VjEIn%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCIGRhldIzSEiSi38FQg8SKy0nbkjetbYc6MOZN8lXbsg7AiB5pY6PoVDS8%2F3qS%2FPqd0zaHjqWrjqvDrAtTiZVSrpKmyrjAwiC%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAIaDDA1OTAwMzU0Njg2NSIMoRUcs%2Ft3M%2Blz7DI3KrcDKVODcJNpq1tNel77UHdAJVSUDWRkG7Fx5loyHhQsb2V3MFfHqAGKJpbdr7xHlDY7CaTl5nrTdNe91InXfpM25fFyYQlANwrhlcYGiPEsN14mSBe%2F5gy0gipiPaN835Viwb0O1Raw9MP9Clc84q%2BHvW5YyQ02NIZYE7OlHYSWN6mbI0F9r7reRa2zNLjFVcvJRkAyt4I9L1Pbehf4WwFF1Er0ResczdJ2FbwKCZNaKWznwhdjVp8hw0SFC%2F9uxnDHzu8DTPeutA3mHeUetMOEtKspkSvIj%2FWQDI1OTECcNv0N7CfmUKSh9m5tOYn%2Fb7KQx8pPA450Snd3faB7euDzPwvCmtfeUzYs0n0UxWt9JWXxKONmvrMlxf9bjZL0RBR1NNeNgwFuCCCRZDAKogbBI%2FclJmPUiNO38mHO10B1unMow9XtojodS3U6Iz2n8jeERhaR4Rt4KeWB9ojZTQhUr5d1uUX%2FVpmJmFQhgvPXmEi%2Fnlp65TXeZNdbUncQPqZ8PaKGYxJn1F%2FwnuJH1Ww6ksTur4rcggGAEHTBgXL5vmLjJG2ZhH1vxaoY8qtUO5EuXwVVfBXCtzCit9jmBTq1Abb%2BixDckXLEYAwidBhA%2B4MKpbhmH4LItnjE3tSuFgOHFbwaq9g0MZyGtL3OUxCKc3eAkkLzOrJnEag%2F2eV%2BdMcU9%2F%2BKI5h8yQsz5PEFeqkY3BID%2BY7oIU6qhqhb38PVrbt1oyF420cS%2BoSpt2Nj6E%2FuCZZTrgMakE%2B9QXvAaTIBWG%2Fc0xOv6d3rGTAnZTkkscgD7j%2FP1jqFkf388YT5jCbAWlx8OiMbj%2BVPkK8UvXhgSh1ZPMk%3D&AWSAccessKeyId=ASIAQ3PHCVTY6VYAY5FB&Expires=1557539079&Signature=HF7E%2BOACBDJPtLAerMWH6MTKDCw%3D&hash=8fe7c8a1c5599e4659a2b26919b2a59f4aff8c452e5a07793dc187e49c69dc9e&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0957417414004060&tid=spdf-7bd6a860-4c57-4e39-87ce-bd2f050badcf&sid=57f9f09d5d026341242a7bd2fb38b97c5897gxrqb&type=client)

**[37] \[ICDAR-2015]** D. Karatzas, L. Gomez-Bigorda, A. Nicolaou, S. Ghosh, A. Bagdanov, M. Iwamura, J. Matas, L. Neumann, V. R. Chandrasekhar, S. Lu et al., “ICDAR 2015 competition on robust reading,” in Proceedings of ICDAR, 2015, pp. 1156–1160. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7333942)

**[38] \[arXiv-2016]** A. Veit, T. Matera, L. Neumann, J. Matas, and S. Belongie, “Coco-text: Dataset and benchmark for text detection and recognition in natural images,” CoRR abs/1601.07140, 2016. [paper](https://arxiv.org/pdf/1601.07140.pdf) [code](https://github.com/andreasveit/coco-text)

**[39] \[IJDAR-2019]** C.-K. Ch’ng, C. S. Chan, and C.-L. Liu, “Total-text: toward orientation robustness in scene text detection,” International Journal on Document Analysis and Recognition (IJDAR), pp. 1–22, 2019. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8270088) [code](https://github.com/cs-chan/Total-Text-Dataset)

**[40] \[ICDAR-2017]** B. Shi, C. Yao, M. Liao, M. Yang, P. Xu, L. Cui, S. Belongie, S. Lu, and X. Bai, “ICDAR2017 competition on reading chinese text in the wild (rctw-17),” in Proceedings of ICDAR, 2017, pp. 1429–1434. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8270164)

**[41] \[ICPR-2018]** M. He, Y. Liu, Z. Yang, S. Zhang, C. Luo, F. Gao, Q. Zheng, Y. Wang, X. Zhang, and L. Jin, “ICPR2018 contest on robust reading for multi-type web images,” in Proceedings of ICPR, 2018, pp. 7–12. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8546143)

**[42] \[JCS&T-2019]** Yuan T L, Zhu Z, Xu K, et al. A large chinese text dataset in the wild[J]. Journal of Computer Science and Technology, 2019, 34(3): 509-521. [paper](https://link.springer.com/article/10.1007/s11390-019-1923-y) [code](https://github.com/yuantailing/ctw-baseline)

**[43] \[arXiv-2017]** L. Yuliang, J. Lianwen, Z. Shuaitao, and Z. Sheng, “Detecting curve text in the wild: New dataset and new solution,” CoRR abs/1712.02170, 2017. [paper](https://arxiv.org/pdf/1712.02170.pdf) [code](https://github.com/Yuliang-Liu/Curve-Text-Detector)

**[44] \[ECCV-2018]** P. Lyu, M. Liao, C. Yao, W. Wu, and X. Bai, “Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes,” in Proceedings of ECCV, 2018, pp. 67–83. [paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Pengyuan_Lyu_Mask_TextSpotter_An_ECCV_2018_paper.pdf) [code](https://github.com/lvpengyuan/masktextspotter.caffe2)

**[45] \[NIPS-W-2011]** Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” in Proceedings of NIPS, 2011. [paper](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/37648.pdf)

**[46] \[PR-2019]** C. Luo, L. Jin, and Z. Sun, “MORAN: A multi-object rectified attention network for scene text recognition,” Pattern Recognition, vol. 90, pp. 109–118, 2019. [paper](https://pdf.sciencedirectassets.com/272206/1-s2.0-S0031320319X00023/1-s2.0-S0031320319300263/main.pdf?x-amz-security-token=AgoJb3JpZ2luX2VjEHcaCXVzLWVhc3QtMSJIMEYCIQCceDsmz9xCoE%2FnPRGjEfK6RAdvbVW7B%2B7rvG4viZPq%2BAIhALnS8lFI9N7LE3%2BNey3EgbWZU8f%2BJIAJIxlk5ewtKNTgKtoDCG8QAhoMMDU5MDAzNTQ2ODY1IgxbFmRrlDI1X0pjiscqtwMyG%2F2gaQjY2Ol3wnc2MtQwmfUOBotnxAj7F7lPnLyeRqPWJo6swI52Tz0YWuSI%2FTXRk3wuLNZthvTkLRWlD5wejFNGIAM9VNUuaYmfhLPT%2Ft33yaDBMGBe3wP%2BdsH0OXHn7JtbzLAFK%2BFPBYXLfSMth4IbmdKzLPAR77noWqY3NvtbYRJEvpw4r9N7yvM%2Fo1lBQnoulBsX%2B7sKRKm9SxWrreldzSuX2EIbnk5FPyXAkTlzYfH%2FCjoTOGYrReZl1VeFSnml73TaF1RImYbO0t155QfM8X90oEMKxlfd1IU8PzuYy14%2Bo0TAmHB3kYh3aKN6CEZ%2FEAHhxxhGrysEhBZWLF0RMp2oZFGDPxiOJVo3QES2GR38d9uBYjXF0dzjxgFnn40SuYTRE29nIjspgjZbsqeWyP%2BbsFHJcteX3w0eN6wq%2B%2Bbd5yPuAELoAy246KPZvRwBXGYUH1%2Bgm%2BqIIKidMGUedx98L2%2FO%2FAnbQ7gPCKW9HIWRdExufStWHid2r8gPpwB3%2Fb%2FKtCbA7iqm7cedZaqgjvaBbKM0hgBlgfXMGJTniXSmGYAIhvgH0aAlUCAAGeNsovbGMMCw1OYFOrMBESogEL75zcwF5QSaTC8s2DdSVFWjhwza7lgexCXY7r6aW4Y80XYluSj6%2BPWj2x6qH23kIlPIEfmyXL%2FJbbBzxTh%2FBbA9UVu04wph8eB%2BNGtvajIx%2FZFVNJSHc3WHz2DWlSAkLnuNGtahDLdXTG3ZD9jhdbweN7QdKYXsPPLh6ysos7v0hO9f7mM2%2BWsA5yOwB8lg9d2xmYu2PU6RKCQcv8hC1ASHgZh5PiYGBUjTfdv0cfI%3D&AWSAccessKeyId=ASIAQ3PHCVTYXA3CRTFK&Expires=1557473755&Signature=eBwdaj8gPpxtBnisCTPveLqX3iw%3D&hash=6f612616f8fba56ffd779d1eee8bfb19e39b7276f2b922ed529a46e51075049d&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0031320319300263&tid=spdf-5d02711b-ea3c-46c1-bb55-4d301d13f947&sid=57f9f09d5d026341242a7bd2fb38b97c5897gxrqb&type=client) [code](https://github.com/Canjie-Luo/MORAN_v2)

**[47] \[ACM-2019]** Xie H, Fang S, Zha Z J, et al, “Convolutional Attention Networks for Scene Text Recognition,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 15, pp. 3 2019. [paper](https://dl.acm.org/citation.cfm?id=3231737)

**[48] \[AAAI-2019]** M. Liao, J. Zhang, Z. Wan, F. Xie, J. Liang, P. Lyu, C. Yao, and X. Bai, “Scene text recognition from two-dimensional perspective,” in Proceedings of AAAI, 2019, pp. 8714–8721. [paper](https://arxiv.org/pdf/1809.06508.pdf)

**[49] \[AAAI-2019]** H. Li, P. Wang, C. Shen, and G. Zhang, “Show, attend and read: A simple and strong baseline for irregular text recognition,” in Proceedings of AAAI, 2019, pp. 8610–8617. [paper](https://arxiv.org/pdf/1811.00751.pdf) [code](https://tinyurl.com/ShowAttendRead)

**[53] \[NIPS-W-2014]** M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman, “Synthetic data and artificial neural networks for natural scene text recognition,” in Proceedings of NIPS-W, 2014. [paper](https://arxiv.org/pdf/1406.2227.pdf) [code](http://www.robots.ox.ac.uk/~vgg/publications/2014/Jaderberg14c/)

**[54] \[CVPR-2016]** A. Gupta, A. Vedaldi, and A. Zisserman, “Synthetic data for text
localisation in natural images,” in Proceedings of CVPR, 2016, pp. 2315–2324. [paper](http://www.robots.ox.ac.uk/~ankush/textloc.pdf) [code](https://github.com/ankush-me/SynthText)

**[55] \[CVPR-2019]** F. Zhan and S. Lu, “ESIR: End-to-end scene text recognition via iterative image rectification,” in Proceedings of CVPR, 2019, pp. 2059–2068. [paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhan_ESIR_End-To-End_Scene_Text_Recognition_via_Iterative_Image_Rectification_CVPR_2019_paper.pdf)

**[56] \[CVPR-2019]** Y. Zhang, S. Nie, W. Liu, X. Xu, D. Zhang, and H. T. Shen, “Sequence-to-sequence domain adaptation network for robust text image recognition,” in Proceedings of CVPR, 2019, pp. 2740–2749. [paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Sequence-To-Sequence_Domain_Adaptation_Network_for_Robust_Text_Image_Recognition_CVPR_2019_paper.pdf) [code](https://github.com/AprilYapingZhang/Seq2SeqAdapt)

**[57]\[ICDAR-2019]** Y. Sun, Z. Ni, C.-K. Chng, Y. Liu, C. Luo, C. C. Ng, J. Han, E. Ding, J. Liu, D. Karatzas et al., “ICDAR 2019 competition on large-scale street view text with partial labeling–RRC-LSVT,” in Proceedings of ICDAR, 2019, pp. 1557–1562. [paper](https://arxiv.org/abs/1909.07741) [Link](https://rrc.cvc.uab.es/?ch=16)

**[58]\[ICDAR-2019]** C.-K. Chng, Y. Liu, Y. Sun, C. C. Ng, C. Luo, Z. Ni, C. Fang, S. Zhang, J. Han, E. Ding et al., “ICDAR2019 robust reading challenge on arbitrary-shaped text (RRC-ArT),” in Proceedings of ICDAR, 2019, pp. 1571–1576. [paper](https://arxiv.org/pdf/1909.07145.pdf) [Link](https://rrc.cvc.uab.es/?ch=14)

**[59]\[ICDAR-2019]** X. Liu, R. Zhang, Y. Zhou, Q. Jiang, Q. Song, N. Li, K. Zhou, L. Wang, D. Wang, M. Liao et al., “ICDAR 2019 robust reading challenge on reading chinese text on signboard,” in Proceedings of ICDAR, 2019, pp. 1577–1581. [paper](https://ieeexplore.ieee.org/abstract/document/8978135) [Link](https://rrc.cvc.uab.es/?ch=12)

**[60] \[NC-2019]** X. Chen, T. Wang, Y. Zhu, L. Jin, and C. Luo, “Adaptive embedding gate for attention-based scene text recognition,” Neurocomputing, vol. 381, pp. 261–271, 2020. [paper](https://www.sciencedirect.com/science/article/pii/S0925231219316510)

***

**Newly added references (Dec 24, 2019)**

**[61]** **\[ICFHR-2018]** C. Wang, F. Yin, and C.-L. Liu, “Memory-augmented attention model for scene text recognition,” in Proceedings of ICFHR, 2018, pp. 62–67. [paper](https://ieeexplore.ieee.org/abstract/document/8563227)

**[62]** **\[ICCV-2019]** M. Yang, Y. Guan, M. Liao, X. He, K. Bian, S. Bai, C. Yao, and X. Bai, “Symmetry-constrained rectification network for scene text recognition,” in Proceedings of ICCV, 2019, pp. 9147–9156. [paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_Symmetry-Constrained_Rectification_Network_for_Scene_Text_Recognition_ICCV_2019_paper.pdf)

**[63]** **\[ICCV-2019]** Y. Sun, J. Liu, W. Liu, J. Han, E. Ding, and J. Liu, “Chinese street view text: Large-scale chinese text reading with partially supervised learning,” in Proceedings of ICCV, 2019, pp. 9086–9095. [paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Sun_Chinese_Street_View_Text_Large-Scale_Chinese_Text_Reading_With_Partially_ICCV_2019_paper.pdf)

**[64]** **\[ICME-2019]** S. Wang, Y. Wang, X. Qin, Q. Zhao, and Z. Tang, “Scene text recognition via gated cascade attention,” in Proceedings of ICME, 2019, pp. 1018–1023. [paper](https://ieeexplore.ieee.org/abstract/document/8784914)

**[65]** **\[ICCV-2019]** J. Baek, G. Kim, J. Lee, S. Park, D. Han, S. Yun, S. J. Oh, and H. Lee, “What is wrong with scene text recognition model comparisons? dataset and model analysis,” in Proceedings of ICCV, 2019, pp. 4714–4722. [paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Baek_What_Is_Wrong_With_Scene_Text_Recognition_Model_Comparisons_Dataset_ICCV_2019_paper.pdf) [code](https://github.com/clovaai/deep-text-recognition-benchmark)

**[66]** **\[Nips-2017]** J. Wang and X. Hu, “Gated recurrent convolution neural network
for OCR,” in Proceedings of NIPS, 2017, pp. 335–344. [paper](http://papers.nips.cc/paper/6637-gated-recurrent-convolution-neural-network-for-ocr.pdf) [code](https://github.com/Jianfeng1991/GRCNN-for-OCR)

**[67]** **\[NC-2020]** Y. Huang, Z. Sun, L. Jin, and C. Luo, “EPAN: Effective parts attention network for scene text recognition,” Neurocomputing, vol. 376, pp. 202–213, 2020. [paper](https://www.sciencedirect.com/science/article/pii/S0925231219313839)

**[68]** **\[NC-2019]** Y. Gao, Y. Chen, J. Wang, M. Tang, and H. Lu, “Reading scene text with fully convolutional sequence modeling,” Neurocomputing, vol. 339, pp. 161–170, 2019. [paper](https://www.sciencedirect.com/science/article/pii/S0925231219301870)

**[69]** **\[ICDAR-W-2019]** X. Qi, Y. Chen, R. Xiao, C.-G. Li, Q. Zou, and S. Cui, “A novel joint character categorization and localization approach for characterlevel scene text recognition,” in Proceedings of ICDAR: Workshops, 2019, pp. 83–90. [paper](https://ieeexplore.ieee.org/abstract/document/8892915)

**[70]** **\[ICDAR-W-2019]** Q. Wang, W. Jia, X. He, Y. Lu, M. Blumenstein, Y. Huang, and S. Lyu, “ReELFA: A scene text recognizer with encoded location and focused attention,” in Proceedings of ICDAR: Workshops, 2019, pp. 71–76. [paper](https://ieeexplore.ieee.org/abstract/document/8892900)

**[71]** **\[ICIP-2019]** Y. Zhu, S. Wang, Z. Huang, and K. Chen, “Text recognition in images based on transformer with hierarchical attention,” in Proceedings of ICIP, 2019, pp. 1945–1949. [paper](https://ieeexplore.ieee.org/abstract/document/8803203)

**[72]** **\[CVPR-2019]** F. Zhan, H. Zhu, and S. Lu, “Spatial fusion gan for image synthesis,” in Proceedings of CVPR, 2019, pp. 3653–3662. [paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhan_Spatial_Fusion_GAN_for_Image_Synthesis_CVPR_2019_paper.pdf)

**[73]** **\[ECCV-2018]** F. Zhan, S. Lu, and C. Xue, “Verisimilar image synthesis for accurate detection and recognition of texts in scenes,” in Proceedings of ECCV, 2018, pp. 249–266. [paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Fangneng_Zhan_Verisimilar_Image_Synthesis_ECCV_2018_paper.pdf) [code](https://github.com/fnzhan/Verisimilar-Image-Synthesis-for-Accurate-Detection-and-Recognition-of-Texts-in-Scenes)

**[74]** **\[MultiMedia-2018]** S. Fang, H. Xie, Z.-J. Zha, N. Sun, J. Tan, and Y. Zhang, “Attention and language ensemble for scene text recognition with convolutional sequence modeling,” in ACM Multimedia Conference on Multimedia Conference, 2018, pp. 248–256. [paper](https://dl.acm.org/citation.cfm?id=3240571) [code](https://github.com/FangShancheng/conv-ensemble-str)

**[75]** **\[Nips-2018]** H. Liu, S. Jin, and C. Zhang, “Connectionist temporal classification with maximum entropy regularization,” in Proceedings of NIPS, 2018, pp. 831–841 . [paper](http://papers.nips.cc/paper/7363-connectionist-temporal-classification-with-maximum-entropy-regularization) [code](https://github.com/liuhu-bigeye/enctc.crnn )

**[76]** **\[PR-2017]** B. Su and S. Lu, “Accurate recognition of words in scenes without
character segmentation using recurrent neural network,” Pattern Recognition, vol. 63, pp. 397–405, 2017. [paper](https://www.sciencedirect.com/science/article/pii/S0031320316303314)

**[77]** **\[CVIU-2016]** A. Mishra, K. Alahari, and C. Jawahar, “Enhancing energy minimization framework for scene text recognition with top-down cues,” Computer Vision and Image Understanding, vol. 145, pp. 30–42, 2016. [paper](https://www.sciencedirect.com/science/article/pii/S107731421600014X)

**[78]** **\[ICPR-2016]** X. Liu, T. Kawanishi, X. Wu, and K. Kashino, “Scene text recognition with CNN classifier and WFST-based word labeling,” in Proceedings of ICPR, 2016, pp. 3999–4004. [paper](https://ieeexplore.ieee.org/abstract/document/7900259)

**[79]** **\[TPAMI-2019]** M. Liao, P. Lyu, M. He, C. Yao, W. Wu, and X. Bai, “Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes,” IEEE Trans. Pattern Anal. Mach. Intell, 2019. [paper](https://ieeexplore.ieee.org/abstract/document/8812908) [code](https://github.com/MhLiao/MaskTextSpotter)

**[80]** **\[AAAI-2020]** T. Wang, Y. Zhu, L. Jin, C. Luo, X. Chen, Y. Wu, Q. Wang, and M. Cai, “Decoupled attention network for text recognition,” in Proceedings of AAAI, 2020. [paper](https://arxiv.org/pdf/1912.10205.pdf) [code](https://github.com/Wang-Tianwei/Decoupled-attention-network)

---

**Newly added references (Feb 29, 2020)**

**[81]** **\[ICDAR-2019]** N. Nayef, Y. Patel, M. Busta, P. N. Chowdhury, D. Karatzas, W. Khlif, J. Matas, U. Pal, J.-C. Burie, C.-l. Liu et al., “ICDAR2019 robust reading challenge on multi-lingual scene text detection and recognition–RRC-MLT-2019,” in Proceedings of ICDAR, 2019, pp. 1582–1587. [paper](https://arxiv.org/pdf/1907.00945.pdf)

**[82]** **\[arXiv-2019]** W. Wang, E. Xie, P. Sun, W. Wang, L. Tian, C. Shen, and P. Luo, “TextSR: Content-aware text super-resolution guided by recognition,” CoRR abs/1909.07113, 2019. [paper](https://arxiv.org/pdf/1909.07113.pdf) [code](https://github.com/xieenze/TextSR)

**[83]** **\[AAAI-2020]** Z. Wan, M. He, H. Chen, X. Bai, and C. Yao, “Textscanner: Reading characters in order for robust scene text recognition,” In Proceedings of AAAI, 2020. [paper](https://arxiv.org/pdf/1912.12422.pdf)

**[84]** **\[AAAI-2020]** W. Hu, X. Cai, J. Hou, S. Yi, and Z. Lin, “GTC: Guided training of ctc towards efficient and accurate scene text recognition,” In Proceedings of AAAI, 2020. [paper](https://www.aaai.org/Papers/AAAI/2020GB/AAAI-HuW.7838.pdf)

**[85]** **\[IJCV-2020]** C. Luo, Q. Lin, Y. Liu, J. Lianwen, and S. Chunhua, “Separating content from style using adversarial learning for recognizing text in the wild,” Int. J. Comput. Vis. 2020. [paper](https://arxiv.org/pdf/2001.04189.pdf)

---

**Newly added references (May 8, 2020)**

**[86]** **\[CVPR-2020]** R. Litman, O. Anschel, S. Tsiper, R. Litman, S. Mazor, and R. Manmatha, “SCATTER: selective context attentional scene text recognizer,” in Proceedings of CVPR, 2020. [paper](https://arxiv.org/abs/2003.11288.pdf)

**[87]** **\[CVPR-2020]** D. Yu, X. Li, C. Zhang, J. Han, J. Liu, and E. Ding, “Towards accurate scene text recognition with semantic reasoning networks,” in Proceedings of CVPR, 2020. [paper](https://arxiv.org/abs/2003.12294.pdf)

**[88]** **\[CVPR-2020]** S. Long and C. Yao, “UnrealText: Synthesizing realistic scene text images from the unreal world,” in Proceedings of CVPR, 2020. [paper](https://arxiv.org/abs/2003.10608.pdf)

**[89]** **\[ECCV-2016]** W. Qiu and A. L. Yuille, “Unrealcv: Connecting computer vision
to unreal engine,” in Proceedings of ECCV, 2016, pp. 909–916. [paper](https://link.springer.com/chapter/10.1007/978-3-319-49409-8_75)

---

**Newly added references (Dec 8, 2020)**

**[91]** **\[ICVGIP-2018]** Gupta A, Vedaldi A, Zisserman A. "Learning to read by spelling: Towards unsupervised text recognition," in Proceedings of ICVGIP, 2018. [paper](https://dl.acm.org/doi/pdf/10.1145/3293353.3293386)

**[92]** **\[CVPR-2020]** Wan Z, Zhang J, Zhang L, et al, "On Vocabulary Reliance in Scene Text Recognition," in Proceedings of CVPR, 2020. [paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wan_On_Vocabulary_Reliance_in_Scene_Text_Recognition_CVPR_2020_paper.pdf)

**[93]** **\[ECAI-2020]** Bleeker M, de Rijke M, "Bidirectional Scene Text Recognition with a Single Decoder," in Proceedings of ECAI, 2020. [paper](https://arxiv.org/pdf/1912.03656.pdf) [code](https://github.com/MauritsBleeker/Bi-STET)

**[94]** **\[arXiv-2019]** Bartz C, Bethge J, Yang H, et al, "KISS: Keeping It Simple for Scene Text Recognition,"CoRR abs/1911.08400, 2019. [paper](https://arxiv.org/pdf/1911.08400.pdf) [code](https://github.com/Bartzi/kiss)

**[95]** **\[arXiv-2020]** Zhang C, Xu Y, Cheng Z, et al, "SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition," CoRR abs/2005.13117, 2020. [paper](https://arxiv.org/pdf/2005.13117.pdf)

**[96]** **\[arXiv-2020]** Lin J, Cheng Z, Bai F, et al, "Text Recognition in Real Scenarios with a Few Labeled Samples," CoRR abs/2006.12209, 2020. [paper](https://arxiv.org/pdf/2006.12209.pdf)

**[97]** **\[ECCV-2020]** Zhang C, Gupta A, Zisserman A. "Adaptive Text Recognition through Visual Matching," in Proceedings of ECCV, 2020. [paper](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_4) [code]( http://www.robots.ox.ac.uk/~vgg/research/FontAdaptor20/)

**[98]** **\[ECCV-2020]** Zhang H, Yao Q, Yang M, et al, "AutoSTR: Efficient Backbone Search for Scene Text Recognition," in Proceedings of ECCV, 2020. [paper](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123690732.pdf) [code](https://github.com/AutoML-4Paradigm/AutoSTR.git)

**[99]** **\[ECCV-2020]** Yan R, Huang Y, "PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit," in Proceedings of ECCV, 2020. [paper](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600154.pdf)

**[100]** **\[ECCV-2020]** Yue X, Kuang Z, Lin C, et al. RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition," in Proceedings of ECCV, 2020. [paper](https://arxiv.org/pdf/2007.07542.pdf)

**[101]** **\[CVPR-2020]** Zhi Qiao, Yu Zhou, Dongbao Yang, Yucan Zhou, and Weiping Wang. 2020. SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition. In Proceedings of CVPR. [paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Qiao_SEED_Semantics_Enhanced_Encoder-Decoder_Framework_for_Scene_Text_Recognition_CVPR_2020_paper.pdf) [code]( https://github.com/Pay20Y/SEED)

## 6.Help

If you have any problem in our resources, or any good paper/code we missed, please inform us at [email protected]. Thank you for your contribution.

***

## 7.Copyright

Sample