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ASTER: Attentional Scene Text Recognizer with Flexible Rectification

ASTER is an accurate scene text recognizer with flexible rectification mechanism. The research paper can be found here.

ASTER Overview

The implementation of ASTER reuses code from Tensorflow Object Detection API.

Update

[07/13/2019] A PyTorch port has been made by @ayumiymk.

Correction (10/22/2018)

We have identified a bug we accidentally made in the code that causes only part of SVT images being tested and results in higher results. The bug has been fixed in commit a7e8613. Below are the corrected numbers on SVT. The results are still state-of-the-art, so the conclusions are not affected.

  • SVT (50) ASTER: 97.4%; ASTER-A: 96.3%; ASTER-B: 96.1%;
  • SVT (None): ASTER: 89.5%; ASTER-A: 80.2%; ASTER-B: 81.6%

Prerequisites

ASTER was developed and tested with TensorFlow r1.4. Higher versions may not work.

ASTER requires Protocol Buffers (version>=2.6). Besides, in Ubuntu 16.04:

sudo apt install cmake libcupti-dev
pip3 install --user protobuf tqdm numpy editdistance

Installation

  1. Go to c_ops/ and run build.sh to build the custom operators
  2. Execute protoc aster/protos/*.proto --python_out=. to build the protobuf files
  3. Add /path/to/aster to PYTHONPATH, or set this variable for every run

Demo

A demo program is located at aster/demo.py, accompanied with pretrained model files available on our release page. Download model-demo.zip and extract it under aster/experiments/demo/ before running the demo.

To run the demo, simply execute:

python3 aster/demo.py

This will output the recognition result of the demo image and the rectified image.

Training and on-the-fly evaluation

Data preparation scripts for several popular scene text datasets are located under aster/tools. See their source code for usage.

To run the example training, execute

python3 aster/train.py \
--exp_dir experiments/demo \
--num_clones 2

Change the configuration in experiments/aster/trainval.prototxt to configure your own training process.

During the training, you can run a separate program to repeatedly evaluates the produced checkpoints.

python3 aster/eval.py \
--exp_dir experiments/demo

Evaluation configuration is also in trainval.prototxt.

Citation

If you find this project helpful for your research, please cite the following papers:

@article{bshi2018aster,
author = {Baoguang Shi and
Mingkun Yang and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {ASTER: An Attentional Scene Text Recognizer with Flexible Rectification},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {}, number = {}, pages = {1-1},
year = {2018}, }
@inproceedings{ShiWLYB16,
author = {Baoguang Shi and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {Robust Scene Text Recognition with Automatic Rectification},
booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition,
{CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016},
pages = {4168--4176},
year = {2016}
}

IMPORTANT NOTICE: Although this software is licensed under MIT, our intention is to make it free for academic research purposes. If you are going to use it in a product, we suggest you contact us regarding possible patent issues.

About

Recognizing cropped text in natural images.

Topics

computer-visionocrrecognitionscene-text

Resources

Readme
MIT license
Activity

Stars

743 stars

Watchers

18 watching

Forks

196 forks
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Used by

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
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ASTER: Attentional Scene Text Recognizer with Flexible Rectification
ASTER is an accurate scene text recognizer with flexible rectification mechanism. The research paper can be found here.
ASTER Overview
The implementation of ASTER reuses code from Tensorflow Object Detection API.
Update
[07/13/2019] A PyTorch port has been made by @ayumiymk.
Correction (10/22/2018)
We have identified a bug we accidentally made in the code that causes only part of SVT images being tested and results in higher results. The bug has been fixed in commit a7e8613. Below are the corrected numbers on SVT. The results are still state-of-the-art, so the conclusions are not affected.
  • SVT (50) ASTER: 97.4%; ASTER-A: 96.3%; ASTER-B: 96.1%;
  • SVT (None): ASTER: 89.5%; ASTER-A: 80.2%; ASTER-B: 81.6%
Prerequisites
ASTER was developed and tested with TensorFlow r1.4. Higher versions may not work.
ASTER requires Protocol Buffers (version>=2.6). Besides, in Ubuntu 16.04:
sudo apt install cmake libcupti-dev
pip3 install --user protobuf tqdm numpy editdistance

Installation
  1. Go to c_ops/ and run build.sh to build the custom operators
  2. Execute protoc aster/protos/*.proto --python_out=. to build the protobuf files
  3. Add /path/to/aster to PYTHONPATH, or set this variable for every run
Demo
A demo program is located at aster/demo.py, accompanied with pretrained model files available on our release page. Download model-demo.zip and extract it under aster/experiments/demo/ before running the demo.
To run the demo, simply execute:
python3 aster/demo.py

This will output the recognition result of the demo image and the rectified image.
Training and on-the-fly evaluation
Data preparation scripts for several popular scene text datasets are located under aster/tools. See their source code for usage.
To run the example training, execute
python3 aster/train.py \
--exp_dir experiments/demo \
--num_clones 2

Change the configuration in experiments/aster/trainval.prototxt to configure your own training process.
During the training, you can run a separate program to repeatedly evaluates the produced checkpoints.
python3 aster/eval.py \
--exp_dir experiments/demo

Evaluation configuration is also in trainval.prototxt.
Citation
If you find this project helpful for your research, please cite the following papers:
@article{bshi2018aster,
author = {Baoguang Shi and
Mingkun Yang and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {ASTER: An Attentional Scene Text Recognizer with Flexible Rectification},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {}, number = {}, pages = {1-1},
year = {2018}, }
@inproceedings{ShiWLYB16,
author = {Baoguang Shi and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {Robust Scene Text Recognition with Automatic Rectification},
booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition,
{CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016},
pages = {4168--4176},
year = {2016}
}

IMPORTANT NOTICE: Although this software is licensed under MIT, our intention is to make it free for academic research purposes. If you are going to use it in a product, we suggest you contact us regarding possible patent issues.
About
Recognizing cropped text in natural images.
Topics
computer-visionocrrecognitionscene-text
Resources
Readme
MIT license
Activity
Stars
743 stars
Watchers
18 watching
Forks
196 forks
Report repository
Releases
Packages
Used by
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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ASTER: Attentional Scene Text Recognizer with Flexible Rectification
ASTER is an accurate scene text recognizer with flexible rectification mechanism. The research paper can be found here.
ASTER Overview
The implementation of ASTER reuses code from Tensorflow Object Detection API.
Update
[07/13/2019] A PyTorch port has been made by @ayumiymk.
Correction (10/22/2018)
We have identified a bug we accidentally made in the code that causes only part of SVT images being tested and results in higher results. The bug has been fixed in commit a7e8613. Below are the corrected numbers on SVT. The results are still state-of-the-art, so the conclusions are not affected.
  • SVT (50) ASTER: 97.4%; ASTER-A: 96.3%; ASTER-B: 96.1%;
  • SVT (None): ASTER: 89.5%; ASTER-A: 80.2%; ASTER-B: 81.6%
Prerequisites
ASTER was developed and tested with TensorFlow r1.4. Higher versions may not work.
ASTER requires Protocol Buffers (version>=2.6). Besides, in Ubuntu 16.04:
sudo apt install cmake libcupti-dev
pip3 install --user protobuf tqdm numpy editdistance

Installation
  1. Go to c_ops/ and run build.sh to build the custom operators
  2. Execute protoc aster/protos/*.proto --python_out=. to build the protobuf files
  3. Add /path/to/aster to PYTHONPATH, or set this variable for every run
Demo
A demo program is located at aster/demo.py, accompanied with pretrained model files available on our release page. Download model-demo.zip and extract it under aster/experiments/demo/ before running the demo.
To run the demo, simply execute:
python3 aster/demo.py

This will output the recognition result of the demo image and the rectified image.
Training and on-the-fly evaluation
Data preparation scripts for several popular scene text datasets are located under aster/tools. See their source code for usage.
To run the example training, execute
python3 aster/train.py \
--exp_dir experiments/demo \
--num_clones 2

Change the configuration in experiments/aster/trainval.prototxt to configure your own training process.
During the training, you can run a separate program to repeatedly evaluates the produced checkpoints.
python3 aster/eval.py \
--exp_dir experiments/demo

Evaluation configuration is also in trainval.prototxt.
Citation
If you find this project helpful for your research, please cite the following papers:
@article{bshi2018aster,
author = {Baoguang Shi and
Mingkun Yang and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {ASTER: An Attentional Scene Text Recognizer with Flexible Rectification},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {}, number = {}, pages = {1-1},
year = {2018}, }
@inproceedings{ShiWLYB16,
author = {Baoguang Shi and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {Robust Scene Text Recognition with Automatic Rectification},
booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition,
{CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016},
pages = {4168--4176},
year = {2016}
}

IMPORTANT NOTICE: Although this software is licensed under MIT, our intention is to make it free for academic research purposes. If you are going to use it in a product, we suggest you contact us regarding possible patent issues.
About
Recognizing cropped text in natural images.
Topics
computer-visionocrrecognitionscene-text
Resources
Readme
MIT license
Activity
Stars
743 stars
Watchers
18 watching
Forks
196 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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ASTER: Attentional Scene Text Recognizer with Flexible Rectification
ASTER is an accurate scene text recognizer with flexible rectification mechanism. The research paper can be found here.
ASTER Overview
The implementation of ASTER reuses code from Tensorflow Object Detection API.
Update
[07/13/2019] A PyTorch port has been made by @ayumiymk.
Correction (10/22/2018)
We have identified a bug we accidentally made in the code that causes only part of SVT images being tested and results in higher results. The bug has been fixed in commit a7e8613. Below are the corrected numbers on SVT. The results are still state-of-the-art, so the conclusions are not affected.
  • SVT (50) ASTER: 97.4%; ASTER-A: 96.3%; ASTER-B: 96.1%;
  • SVT (None): ASTER: 89.5%; ASTER-A: 80.2%; ASTER-B: 81.6%
Prerequisites
ASTER was developed and tested with TensorFlow r1.4. Higher versions may not work.
ASTER requires Protocol Buffers (version>=2.6). Besides, in Ubuntu 16.04:
sudo apt install cmake libcupti-dev
pip3 install --user protobuf tqdm numpy editdistance

Installation
  1. Go to c_ops/ and run build.sh to build the custom operators
  2. Execute protoc aster/protos/*.proto --python_out=. to build the protobuf files
  3. Add /path/to/aster to PYTHONPATH, or set this variable for every run
Demo
A demo program is located at aster/demo.py, accompanied with pretrained model files available on our release page. Download model-demo.zip and extract it under aster/experiments/demo/ before running the demo.
To run the demo, simply execute:
python3 aster/demo.py

This will output the recognition result of the demo image and the rectified image.
Training and on-the-fly evaluation
Data preparation scripts for several popular scene text datasets are located under aster/tools. See their source code for usage.
To run the example training, execute
python3 aster/train.py \
--exp_dir experiments/demo \
--num_clones 2

Change the configuration in experiments/aster/trainval.prototxt to configure your own training process.
During the training, you can run a separate program to repeatedly evaluates the produced checkpoints.
python3 aster/eval.py \
--exp_dir experiments/demo

Evaluation configuration is also in trainval.prototxt.
Citation
If you find this project helpful for your research, please cite the following papers:
@article{bshi2018aster,
author = {Baoguang Shi and
Mingkun Yang and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {ASTER: An Attentional Scene Text Recognizer with Flexible Rectification},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {}, number = {}, pages = {1-1},
year = {2018}, }
@inproceedings{ShiWLYB16,
author = {Baoguang Shi and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {Robust Scene Text Recognition with Automatic Rectification},
booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition,
{CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016},
pages = {4168--4176},
year = {2016}
}

IMPORTANT NOTICE: Although this software is licensed under MIT, our intention is to make it free for academic research purposes. If you are going to use it in a product, we suggest you contact us regarding possible patent issues.
About
Recognizing cropped text in natural images.
Topics
computer-visionocrrecognitionscene-text
Resources
Readme
MIT license
Activity
Stars
743 stars
Watchers
18 watching
Forks
196 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
ASTER: Attentional Scene Text Recognizer with Flexible Rectification
ASTER is an accurate scene text recognizer with flexible rectification mechanism. The research paper can be found here.
ASTER Overview
The implementation of ASTER reuses code from Tensorflow Object Detection API.
Update
[07/13/2019] A PyTorch port has been made by @ayumiymk.
Correction (10/22/2018)
We have identified a bug we accidentally made in the code that causes only part of SVT images being tested and results in higher results. The bug has been fixed in commit a7e8613. Below are the corrected numbers on SVT. The results are still state-of-the-art, so the conclusions are not affected.
  • SVT (50) ASTER: 97.4%; ASTER-A: 96.3%; ASTER-B: 96.1%;
  • SVT (None): ASTER: 89.5%; ASTER-A: 80.2%; ASTER-B: 81.6%
Prerequisites
ASTER was developed and tested with TensorFlow r1.4. Higher versions may not work.
ASTER requires Protocol Buffers (version>=2.6). Besides, in Ubuntu 16.04:
sudo apt install cmake libcupti-dev
pip3 install --user protobuf tqdm numpy editdistance

Installation
  1. Go to c_ops/ and run build.sh to build the custom operators
  2. Execute protoc aster/protos/*.proto --python_out=. to build the protobuf files
  3. Add /path/to/aster to PYTHONPATH, or set this variable for every run
Demo
A demo program is located at aster/demo.py, accompanied with pretrained model files available on our release page. Download model-demo.zip and extract it under aster/experiments/demo/ before running the demo.
To run the demo, simply execute:
python3 aster/demo.py

This will output the recognition result of the demo image and the rectified image.
Training and on-the-fly evaluation
Data preparation scripts for several popular scene text datasets are located under aster/tools. See their source code for usage.
To run the example training, execute
python3 aster/train.py \
--exp_dir experiments/demo \
--num_clones 2

Change the configuration in experiments/aster/trainval.prototxt to configure your own training process.
During the training, you can run a separate program to repeatedly evaluates the produced checkpoints.
python3 aster/eval.py \
--exp_dir experiments/demo

Evaluation configuration is also in trainval.prototxt.
Citation
If you find this project helpful for your research, please cite the following papers:
@article{bshi2018aster,
author = {Baoguang Shi and
Mingkun Yang and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {ASTER: An Attentional Scene Text Recognizer with Flexible Rectification},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {}, number = {}, pages = {1-1},
year = {2018}, }
@inproceedings{ShiWLYB16,
author = {Baoguang Shi and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {Robust Scene Text Recognition with Automatic Rectification},
booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition,
{CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016},
pages = {4168--4176},
year = {2016}
}

IMPORTANT NOTICE: Although this software is licensed under MIT, our intention is to make it free for academic research purposes. If you are going to use it in a product, we suggest you contact us regarding possible patent issues.
About
Recognizing cropped text in natural images.
Topics
computer-visionocrrecognitionscene-text
Resources
Readme
MIT license
Activity
Stars
743 stars
Watchers
18 watching
Forks
196 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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ASTER: Attentional Scene Text Recognizer with Flexible Rectification
ASTER is an accurate scene text recognizer with flexible rectification mechanism. The research paper can be found here.
ASTER Overview
The implementation of ASTER reuses code from Tensorflow Object Detection API.
Update
[07/13/2019] A PyTorch port has been made by @ayumiymk.
Correction (10/22/2018)
We have identified a bug we accidentally made in the code that causes only part of SVT images being tested and results in higher results. The bug has been fixed in commit a7e8613. Below are the corrected numbers on SVT. The results are still state-of-the-art, so the conclusions are not affected.
  • SVT (50) ASTER: 97.4%; ASTER-A: 96.3%; ASTER-B: 96.1%;
  • SVT (None): ASTER: 89.5%; ASTER-A: 80.2%; ASTER-B: 81.6%
Prerequisites
ASTER was developed and tested with TensorFlow r1.4. Higher versions may not work.
ASTER requires Protocol Buffers (version>=2.6). Besides, in Ubuntu 16.04:
sudo apt install cmake libcupti-dev
pip3 install --user protobuf tqdm numpy editdistance

Installation
  1. Go to c_ops/ and run build.sh to build the custom operators
  2. Execute protoc aster/protos/*.proto --python_out=. to build the protobuf files
  3. Add /path/to/aster to PYTHONPATH, or set this variable for every run
Demo
A demo program is located at aster/demo.py, accompanied with pretrained model files available on our release page. Download model-demo.zip and extract it under aster/experiments/demo/ before running the demo.
To run the demo, simply execute:
python3 aster/demo.py

This will output the recognition result of the demo image and the rectified image.
Training and on-the-fly evaluation
Data preparation scripts for several popular scene text datasets are located under aster/tools. See their source code for usage.
To run the example training, execute
python3 aster/train.py \
--exp_dir experiments/demo \
--num_clones 2

Change the configuration in experiments/aster/trainval.prototxt to configure your own training process.
During the training, you can run a separate program to repeatedly evaluates the produced checkpoints.
python3 aster/eval.py \
--exp_dir experiments/demo

Evaluation configuration is also in trainval.prototxt.
Citation
If you find this project helpful for your research, please cite the following papers:
@article{bshi2018aster,
author = {Baoguang Shi and
Mingkun Yang and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {ASTER: An Attentional Scene Text Recognizer with Flexible Rectification},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {}, number = {}, pages = {1-1},
year = {2018}, }
@inproceedings{ShiWLYB16,
author = {Baoguang Shi and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {Robust Scene Text Recognition with Automatic Rectification},
booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition,
{CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016},
pages = {4168--4176},
year = {2016}
}

IMPORTANT NOTICE: Although this software is licensed under MIT, our intention is to make it free for academic research purposes. If you are going to use it in a product, we suggest you contact us regarding possible patent issues.
About
Recognizing cropped text in natural images.
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computer-visionocrrecognitionscene-text
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MIT license
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18 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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ASTER: Attentional Scene Text Recognizer with Flexible Rectification
ASTER is an accurate scene text recognizer with flexible rectification mechanism. The research paper can be found here.
ASTER Overview
The implementation of ASTER reuses code from Tensorflow Object Detection API.
Update
[07/13/2019] A PyTorch port has been made by @ayumiymk.
Correction (10/22/2018)
We have identified a bug we accidentally made in the code that causes only part of SVT images being tested and results in higher results. The bug has been fixed in commit a7e8613. Below are the corrected numbers on SVT. The results are still state-of-the-art, so the conclusions are not affected.
  • SVT (50) ASTER: 97.4%; ASTER-A: 96.3%; ASTER-B: 96.1%;
  • SVT (None): ASTER: 89.5%; ASTER-A: 80.2%; ASTER-B: 81.6%
Prerequisites
ASTER was developed and tested with TensorFlow r1.4. Higher versions may not work.
ASTER requires Protocol Buffers (version>=2.6). Besides, in Ubuntu 16.04:
sudo apt install cmake libcupti-dev
pip3 install --user protobuf tqdm numpy editdistance

Installation
  1. Go to c_ops/ and run build.sh to build the custom operators
  2. Execute protoc aster/protos/*.proto --python_out=. to build the protobuf files
  3. Add /path/to/aster to PYTHONPATH, or set this variable for every run
Demo
A demo program is located at aster/demo.py, accompanied with pretrained model files available on our release page. Download model-demo.zip and extract it under aster/experiments/demo/ before running the demo.
To run the demo, simply execute:
python3 aster/demo.py

This will output the recognition result of the demo image and the rectified image.
Training and on-the-fly evaluation
Data preparation scripts for several popular scene text datasets are located under aster/tools. See their source code for usage.
To run the example training, execute
python3 aster/train.py \
--exp_dir experiments/demo \
--num_clones 2

Change the configuration in experiments/aster/trainval.prototxt to configure your own training process.
During the training, you can run a separate program to repeatedly evaluates the produced checkpoints.
python3 aster/eval.py \
--exp_dir experiments/demo

Evaluation configuration is also in trainval.prototxt.
Citation
If you find this project helpful for your research, please cite the following papers:
@article{bshi2018aster,
author = {Baoguang Shi and
Mingkun Yang and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {ASTER: An Attentional Scene Text Recognizer with Flexible Rectification},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {}, number = {}, pages = {1-1},
year = {2018}, }
@inproceedings{ShiWLYB16,
author = {Baoguang Shi and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {Robust Scene Text Recognition with Automatic Rectification},
booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition,
{CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016},
pages = {4168--4176},
year = {2016}
}

IMPORTANT NOTICE: Although this software is licensed under MIT, our intention is to make it free for academic research purposes. If you are going to use it in a product, we suggest you contact us regarding possible patent issues.
About
Recognizing cropped text in natural images.
Topics
computer-visionocrrecognitionscene-text
Resources
Readme
MIT license
Activity
Stars
743 stars
Watchers
18 watching
Forks
196 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
ASTER: Attentional Scene Text Recognizer with Flexible Rectification
ASTER is an accurate scene text recognizer with flexible rectification mechanism. The research paper can be found here.
ASTER Overview
The implementation of ASTER reuses code from Tensorflow Object Detection API.
Update
[07/13/2019] A PyTorch port has been made by @ayumiymk.
Correction (10/22/2018)
We have identified a bug we accidentally made in the code that causes only part of SVT images being tested and results in higher results. The bug has been fixed in commit a7e8613. Below are the corrected numbers on SVT. The results are still state-of-the-art, so the conclusions are not affected.
  • SVT (50) ASTER: 97.4%; ASTER-A: 96.3%; ASTER-B: 96.1%;
  • SVT (None): ASTER: 89.5%; ASTER-A: 80.2%; ASTER-B: 81.6%
Prerequisites
ASTER was developed and tested with TensorFlow r1.4. Higher versions may not work.
ASTER requires Protocol Buffers (version>=2.6). Besides, in Ubuntu 16.04:
sudo apt install cmake libcupti-dev
pip3 install --user protobuf tqdm numpy editdistance

Installation
  1. Go to c_ops/ and run build.sh to build the custom operators
  2. Execute protoc aster/protos/*.proto --python_out=. to build the protobuf files
  3. Add /path/to/aster to PYTHONPATH, or set this variable for every run
Demo
A demo program is located at aster/demo.py, accompanied with pretrained model files available on our release page. Download model-demo.zip and extract it under aster/experiments/demo/ before running the demo.
To run the demo, simply execute:
python3 aster/demo.py

This will output the recognition result of the demo image and the rectified image.
Training and on-the-fly evaluation
Data preparation scripts for several popular scene text datasets are located under aster/tools. See their source code for usage.
To run the example training, execute
python3 aster/train.py \
--exp_dir experiments/demo \
--num_clones 2

Change the configuration in experiments/aster/trainval.prototxt to configure your own training process.
During the training, you can run a separate program to repeatedly evaluates the produced checkpoints.
python3 aster/eval.py \
--exp_dir experiments/demo

Evaluation configuration is also in trainval.prototxt.
Citation
If you find this project helpful for your research, please cite the following papers:
@article{bshi2018aster,
author = {Baoguang Shi and
Mingkun Yang and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {ASTER: An Attentional Scene Text Recognizer with Flexible Rectification},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {}, number = {}, pages = {1-1},
year = {2018}, }
@inproceedings{ShiWLYB16,
author = {Baoguang Shi and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {Robust Scene Text Recognition with Automatic Rectification},
booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition,
{CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016},
pages = {4168--4176},
year = {2016}
}

IMPORTANT NOTICE: Although this software is licensed under MIT, our intention is to make it free for academic research purposes. If you are going to use it in a product, we suggest you contact us regarding possible patent issues.
About
Recognizing cropped text in natural images.
Topics
computer-visionocrrecognitionscene-text
Resources
Readme
MIT license
Activity
Stars
743 stars
Watchers
18 watching
Forks
196 forks
Report repository
Releases
Packages
Used by
Contributors
Languages