BranchesTags

Repository files navigation

TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition

1682662695807

The official code of TPS_PP (IJCAI 2023) Paper Link

TPS++, an attention-enhanced TPS transformation that incorporates the attention mechanism to text rectification for the first time. TPS++ builds a more flexible content-aware rectifier, generating a natural text correction that is easier to read by the subsequent recognizer. This code is based on MMOCR 0.4.0 ( Documentation ) with PyTorch 1.6+.

Code List

  • NRTR + TPS_PP
  • CRNN + TPS_PP
  • ABINet-LV + TPS_PP

Installation

Please refer to Install Guide.

Get Started

Please see Getting Started for the basic usage of MMOCR 0.4.0.

Datasets

The specific configuration of the dataset for training and testing can be found here Dataset Document

testing ├── mixture
│ ├── icdar_2013
│ ├── icdar_2015
│ ├── III5K
│ ├── ct80
│ ├── svt
│ ├── svtp
training
├── mixture
│ ├── Syn90k
│ ├── SynthText

Pretrained Models

Get the pretrained models from BaiduNetdisk(passwd:cd9r), GoogleDrive. checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth

MethodsIIIT5KSVTIC13IC15SVTPCUTEAVG
NRTR + TPS_PP96.394.696.685.789.092.492.4
NRTR + TPS_PP *95.695.197.285.989.890.392.3

First, the model needs to be pre-trained using without TPS_PP (pre-train), and then trained end-to-end with a network that incorporates TPS_PP (checkpoint). * denotes the performance of the implemented code. checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth.

Train

Please refer to the training configuration Training Doc

NRTR+TPS++

Setp 1 : Download NRTRpre_train/nrtr/latest.pth in mmocr_ijcai/nrtr/latest.pth

#Step 2
PORT=1234 ./tools/dist_train.sh configs/textrecog/nrtr/nrtr_tps++.py ./ckpt/ijcai_nrtr_tps_pp 4 --seed=123456 --load-from=mmocr_ijcai/nrtr/nrtr_latest.pth

Testing

Please refer to the testing configuration Testing Doc

Acknowledgement

This code is based on MMOCR

Citation

If you find our method useful for your reserach, please cite

@article{zheng2023tps++,
title={TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition},
author={Zheng, Tianlun and Chen, Zhineng and Bai, Jinfeng and Xie, Hongtao and Jiang, Yu-Gang},
journal={IJCAI},
year={2023}
}

License

This project is released under the Apache 2.0 license.

About

Official Pytorch implementations of TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition (IJCAI 2023）

Topics

ocrocr-recognitionpytorchtps

Resources

Readme
Apache-2.0 license
Activity

Stars

42 stars

Watchers

2 watching

Forks

2 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition
1682662695807
The official code of TPS_PP (IJCAI 2023) Paper Link
TPS++, an attention-enhanced TPS transformation that incorporates the attention mechanism to text rectification for the first time. TPS++ builds a more flexible content-aware rectifier, generating a natural text correction that is easier to read by the subsequent recognizer. This code is based on MMOCR 0.4.0 ( Documentation ) with PyTorch 1.6+.
Code List
  •  NRTR + TPS_PP
  •  CRNN + TPS_PP
  •  ABINet-LV + TPS_PP
Installation
Please refer to Install Guide.
Get Started
Please see Getting Started for the basic usage of MMOCR 0.4.0.
Datasets
The specific configuration of the dataset for training and testing can be found here Dataset Document
testing ├── mixture
│ ├── icdar_2013
│ ├── icdar_2015
│ ├── III5K
│ ├── ct80
│ ├── svt
│ ├── svtp
training
├── mixture
│ ├── Syn90k
│ ├── SynthText

Pretrained Models
Get the pretrained models from BaiduNetdisk(passwd:cd9r), GoogleDrive.
checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth
MethodsIIIT5KSVTIC13IC15SVTPCUTEAVG
NRTR + TPS_PP96.394.696.685.789.092.492.4
NRTR + TPS_PP *95.695.197.285.989.890.392.3
First, the model needs to be pre-trained using without TPS_PP (pre-train), and then trained end-to-end with a network that incorporates TPS_PP (checkpoint). * denotes the performance of the implemented code. checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth.
Train
Please refer to the training configuration Training Doc
NRTR+TPS++
Setp 1 : Download NRTRpre_train/nrtr/latest.pth in mmocr_ijcai/nrtr/latest.pth
#Step 2
PORT=1234 ./tools/dist_train.sh configs/textrecog/nrtr/nrtr_tps++.py ./ckpt/ijcai_nrtr_tps_pp 4 --seed=123456 --load-from=mmocr_ijcai/nrtr/nrtr_latest.pth

Testing
Please refer to the testing configuration Testing Doc
Acknowledgement
This code is based on MMOCR
Citation
If you find our method useful for your reserach, please cite
@article{zheng2023tps++,
title={TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition},
author={Zheng, Tianlun and Chen, Zhineng and Bai, Jinfeng and Xie, Hongtao and Jiang, Yu-Gang},
journal={IJCAI},
year={2023}
}
License
This project is released under the Apache 2.0 license.
About
Official Pytorch implementations of TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition (IJCAI 2023）
Topics
ocrocr-recognitionpytorchtps
Resources
Readme
Apache-2.0 license
Activity
Stars
42 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition
1682662695807
The official code of TPS_PP (IJCAI 2023) Paper Link
TPS++, an attention-enhanced TPS transformation that incorporates the attention mechanism to text rectification for the first time. TPS++ builds a more flexible content-aware rectifier, generating a natural text correction that is easier to read by the subsequent recognizer. This code is based on MMOCR 0.4.0 ( Documentation ) with PyTorch 1.6+.
Code List
  •  NRTR + TPS_PP
  •  CRNN + TPS_PP
  •  ABINet-LV + TPS_PP
Installation
Please refer to Install Guide.
Get Started
Please see Getting Started for the basic usage of MMOCR 0.4.0.
Datasets
The specific configuration of the dataset for training and testing can be found here Dataset Document
testing ├── mixture
│ ├── icdar_2013
│ ├── icdar_2015
│ ├── III5K
│ ├── ct80
│ ├── svt
│ ├── svtp
training
├── mixture
│ ├── Syn90k
│ ├── SynthText

Pretrained Models
Get the pretrained models from BaiduNetdisk(passwd:cd9r), GoogleDrive.
checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth
MethodsIIIT5KSVTIC13IC15SVTPCUTEAVG
NRTR + TPS_PP96.394.696.685.789.092.492.4
NRTR + TPS_PP *95.695.197.285.989.890.392.3
First, the model needs to be pre-trained using without TPS_PP (pre-train), and then trained end-to-end with a network that incorporates TPS_PP (checkpoint). * denotes the performance of the implemented code. checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth.
Train
Please refer to the training configuration Training Doc
NRTR+TPS++
Setp 1 : Download NRTRpre_train/nrtr/latest.pth in mmocr_ijcai/nrtr/latest.pth
#Step 2
PORT=1234 ./tools/dist_train.sh configs/textrecog/nrtr/nrtr_tps++.py ./ckpt/ijcai_nrtr_tps_pp 4 --seed=123456 --load-from=mmocr_ijcai/nrtr/nrtr_latest.pth

Testing
Please refer to the testing configuration Testing Doc
Acknowledgement
This code is based on MMOCR
Citation
If you find our method useful for your reserach, please cite
@article{zheng2023tps++,
title={TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition},
author={Zheng, Tianlun and Chen, Zhineng and Bai, Jinfeng and Xie, Hongtao and Jiang, Yu-Gang},
journal={IJCAI},
year={2023}
}
License
This project is released under the Apache 2.0 license.
About
Official Pytorch implementations of TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition (IJCAI 2023）
Topics
ocrocr-recognitionpytorchtps
Resources
Readme
Apache-2.0 license
Activity
Stars
42 stars
Watchers
2 watching
Forks
2 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
Repository files navigation
TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition
1682662695807
The official code of TPS_PP (IJCAI 2023) Paper Link
TPS++, an attention-enhanced TPS transformation that incorporates the attention mechanism to text rectification for the first time. TPS++ builds a more flexible content-aware rectifier, generating a natural text correction that is easier to read by the subsequent recognizer. This code is based on MMOCR 0.4.0 ( Documentation ) with PyTorch 1.6+.
Code List
  •  NRTR + TPS_PP
  •  CRNN + TPS_PP
  •  ABINet-LV + TPS_PP
Installation
Please refer to Install Guide.
Get Started
Please see Getting Started for the basic usage of MMOCR 0.4.0.
Datasets
The specific configuration of the dataset for training and testing can be found here Dataset Document
testing ├── mixture
│ ├── icdar_2013
│ ├── icdar_2015
│ ├── III5K
│ ├── ct80
│ ├── svt
│ ├── svtp
training
├── mixture
│ ├── Syn90k
│ ├── SynthText

Pretrained Models
Get the pretrained models from BaiduNetdisk(passwd:cd9r), GoogleDrive.
checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth
MethodsIIIT5KSVTIC13IC15SVTPCUTEAVG
NRTR + TPS_PP96.394.696.685.789.092.492.4
NRTR + TPS_PP *95.695.197.285.989.890.392.3
First, the model needs to be pre-trained using without TPS_PP (pre-train), and then trained end-to-end with a network that incorporates TPS_PP (checkpoint). * denotes the performance of the implemented code. checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth.
Train
Please refer to the training configuration Training Doc
NRTR+TPS++
Setp 1 : Download NRTRpre_train/nrtr/latest.pth in mmocr_ijcai/nrtr/latest.pth
#Step 2
PORT=1234 ./tools/dist_train.sh configs/textrecog/nrtr/nrtr_tps++.py ./ckpt/ijcai_nrtr_tps_pp 4 --seed=123456 --load-from=mmocr_ijcai/nrtr/nrtr_latest.pth

Testing
Please refer to the testing configuration Testing Doc
Acknowledgement
This code is based on MMOCR
Citation
If you find our method useful for your reserach, please cite
@article{zheng2023tps++,
title={TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition},
author={Zheng, Tianlun and Chen, Zhineng and Bai, Jinfeng and Xie, Hongtao and Jiang, Yu-Gang},
journal={IJCAI},
year={2023}
}
License
This project is released under the Apache 2.0 license.
About
Official Pytorch implementations of TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition (IJCAI 2023）
Topics
ocrocr-recognitionpytorchtps
Resources
Readme
Apache-2.0 license
Activity
Stars
42 stars
Watchers
2 watching
Forks
2 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");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition
1682662695807
The official code of TPS_PP (IJCAI 2023) Paper Link
TPS++, an attention-enhanced TPS transformation that incorporates the attention mechanism to text rectification for the first time. TPS++ builds a more flexible content-aware rectifier, generating a natural text correction that is easier to read by the subsequent recognizer. This code is based on MMOCR 0.4.0 ( Documentation ) with PyTorch 1.6+.
Code List
  •  NRTR + TPS_PP
  •  CRNN + TPS_PP
  •  ABINet-LV + TPS_PP
Installation
Please refer to Install Guide.
Get Started
Please see Getting Started for the basic usage of MMOCR 0.4.0.
Datasets
The specific configuration of the dataset for training and testing can be found here Dataset Document
testing ├── mixture
│ ├── icdar_2013
│ ├── icdar_2015
│ ├── III5K
│ ├── ct80
│ ├── svt
│ ├── svtp
training
├── mixture
│ ├── Syn90k
│ ├── SynthText

Pretrained Models
Get the pretrained models from BaiduNetdisk(passwd:cd9r), GoogleDrive.
checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth
MethodsIIIT5KSVTIC13IC15SVTPCUTEAVG
NRTR + TPS_PP96.394.696.685.789.092.492.4
NRTR + TPS_PP *95.695.197.285.989.890.392.3
First, the model needs to be pre-trained using without TPS_PP (pre-train), and then trained end-to-end with a network that incorporates TPS_PP (checkpoint). * denotes the performance of the implemented code. checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth.
Train
Please refer to the training configuration Training Doc
NRTR+TPS++
Setp 1 : Download NRTRpre_train/nrtr/latest.pth in mmocr_ijcai/nrtr/latest.pth
#Step 2
PORT=1234 ./tools/dist_train.sh configs/textrecog/nrtr/nrtr_tps++.py ./ckpt/ijcai_nrtr_tps_pp 4 --seed=123456 --load-from=mmocr_ijcai/nrtr/nrtr_latest.pth

Testing
Please refer to the testing configuration Testing Doc
Acknowledgement
This code is based on MMOCR
Citation
If you find our method useful for your reserach, please cite
@article{zheng2023tps++,
title={TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition},
author={Zheng, Tianlun and Chen, Zhineng and Bai, Jinfeng and Xie, Hongtao and Jiang, Yu-Gang},
journal={IJCAI},
year={2023}
}
License
This project is released under the Apache 2.0 license.
About
Official Pytorch implementations of TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition (IJCAI 2023）
Topics
ocrocr-recognitionpytorchtps
Resources
Readme
Apache-2.0 license
Activity
Stars
42 stars
Watchers
2 watching
Forks
2 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");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition
1682662695807
The official code of TPS_PP (IJCAI 2023) Paper Link
TPS++, an attention-enhanced TPS transformation that incorporates the attention mechanism to text rectification for the first time. TPS++ builds a more flexible content-aware rectifier, generating a natural text correction that is easier to read by the subsequent recognizer. This code is based on MMOCR 0.4.0 ( Documentation ) with PyTorch 1.6+.
Code List
  •  NRTR + TPS_PP
  •  CRNN + TPS_PP
  •  ABINet-LV + TPS_PP
Installation
Please refer to Install Guide.
Get Started
Please see Getting Started for the basic usage of MMOCR 0.4.0.
Datasets
The specific configuration of the dataset for training and testing can be found here Dataset Document
testing ├── mixture
│ ├── icdar_2013
│ ├── icdar_2015
│ ├── III5K
│ ├── ct80
│ ├── svt
│ ├── svtp
training
├── mixture
│ ├── Syn90k
│ ├── SynthText

Pretrained Models
Get the pretrained models from BaiduNetdisk(passwd:cd9r), GoogleDrive.
checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth
MethodsIIIT5KSVTIC13IC15SVTPCUTEAVG
NRTR + TPS_PP96.394.696.685.789.092.492.4
NRTR + TPS_PP *95.695.197.285.989.890.392.3
First, the model needs to be pre-trained using without TPS_PP (pre-train), and then trained end-to-end with a network that incorporates TPS_PP (checkpoint). * denotes the performance of the implemented code. checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth.
Train
Please refer to the training configuration Training Doc
NRTR+TPS++
Setp 1 : Download NRTRpre_train/nrtr/latest.pth in mmocr_ijcai/nrtr/latest.pth
#Step 2
PORT=1234 ./tools/dist_train.sh configs/textrecog/nrtr/nrtr_tps++.py ./ckpt/ijcai_nrtr_tps_pp 4 --seed=123456 --load-from=mmocr_ijcai/nrtr/nrtr_latest.pth

Testing
Please refer to the testing configuration Testing Doc
Acknowledgement
This code is based on MMOCR
Citation
If you find our method useful for your reserach, please cite
@article{zheng2023tps++,
title={TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition},
author={Zheng, Tianlun and Chen, Zhineng and Bai, Jinfeng and Xie, Hongtao and Jiang, Yu-Gang},
journal={IJCAI},
year={2023}
}
License
This project is released under the Apache 2.0 license.
About
Official Pytorch implementations of TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition (IJCAI 2023）
Topics
ocrocr-recognitionpytorchtps
Resources
Readme
Apache-2.0 license
Activity
Stars
42 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
BranchesTags
Repository files navigation
TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition
1682662695807
The official code of TPS_PP (IJCAI 2023) Paper Link
TPS++, an attention-enhanced TPS transformation that incorporates the attention mechanism to text rectification for the first time. TPS++ builds a more flexible content-aware rectifier, generating a natural text correction that is easier to read by the subsequent recognizer. This code is based on MMOCR 0.4.0 ( Documentation ) with PyTorch 1.6+.
Code List
  •  NRTR + TPS_PP
  •  CRNN + TPS_PP
  •  ABINet-LV + TPS_PP
Installation
Please refer to Install Guide.
Get Started
Please see Getting Started for the basic usage of MMOCR 0.4.0.
Datasets
The specific configuration of the dataset for training and testing can be found here Dataset Document
testing ├── mixture
│ ├── icdar_2013
│ ├── icdar_2015
│ ├── III5K
│ ├── ct80
│ ├── svt
│ ├── svtp
training
├── mixture
│ ├── Syn90k
│ ├── SynthText

Pretrained Models
Get the pretrained models from BaiduNetdisk(passwd:cd9r), GoogleDrive.
checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth
MethodsIIIT5KSVTIC13IC15SVTPCUTEAVG
NRTR + TPS_PP96.394.696.685.789.092.492.4
NRTR + TPS_PP *95.695.197.285.989.890.392.3
First, the model needs to be pre-trained using without TPS_PP (pre-train), and then trained end-to-end with a network that incorporates TPS_PP (checkpoint). * denotes the performance of the implemented code. checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth.
Train
Please refer to the training configuration Training Doc
NRTR+TPS++
Setp 1 : Download NRTRpre_train/nrtr/latest.pth in mmocr_ijcai/nrtr/latest.pth
#Step 2
PORT=1234 ./tools/dist_train.sh configs/textrecog/nrtr/nrtr_tps++.py ./ckpt/ijcai_nrtr_tps_pp 4 --seed=123456 --load-from=mmocr_ijcai/nrtr/nrtr_latest.pth

Testing
Please refer to the testing configuration Testing Doc
Acknowledgement
This code is based on MMOCR
Citation
If you find our method useful for your reserach, please cite
@article{zheng2023tps++,
title={TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition},
author={Zheng, Tianlun and Chen, Zhineng and Bai, Jinfeng and Xie, Hongtao and Jiang, Yu-Gang},
journal={IJCAI},
year={2023}
}
License
This project is released under the Apache 2.0 license.
About
Official Pytorch implementations of TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition (IJCAI 2023）
Topics
ocrocr-recognitionpytorchtps
Resources
Readme
Apache-2.0 license
Activity
Stars
42 stars
Watchers
2 watching
Forks
2 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
TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition
1682662695807
The official code of TPS_PP (IJCAI 2023) Paper Link
TPS++, an attention-enhanced TPS transformation that incorporates the attention mechanism to text rectification for the first time. TPS++ builds a more flexible content-aware rectifier, generating a natural text correction that is easier to read by the subsequent recognizer. This code is based on MMOCR 0.4.0 ( Documentation ) with PyTorch 1.6+.
Code List
  •  NRTR + TPS_PP
  •  CRNN + TPS_PP
  •  ABINet-LV + TPS_PP
Installation
Please refer to Install Guide.
Get Started
Please see Getting Started for the basic usage of MMOCR 0.4.0.
Datasets
The specific configuration of the dataset for training and testing can be found here Dataset Document
testing ├── mixture
│ ├── icdar_2013
│ ├── icdar_2015
│ ├── III5K
│ ├── ct80
│ ├── svt
│ ├── svtp
training
├── mixture
│ ├── Syn90k
│ ├── SynthText

Pretrained Models
Get the pretrained models from BaiduNetdisk(passwd:cd9r), GoogleDrive.
checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth
MethodsIIIT5KSVTIC13IC15SVTPCUTEAVG
NRTR + TPS_PP96.394.696.685.789.092.492.4
NRTR + TPS_PP *95.695.197.285.989.890.392.3
First, the model needs to be pre-trained using without TPS_PP (pre-train), and then trained end-to-end with a network that incorporates TPS_PP (checkpoint). * denotes the performance of the implemented code. checkpoint model in model/xxx/latest.pth, pre-train model in pre_train/xxx/latest.pth.
Train
Please refer to the training configuration Training Doc
NRTR+TPS++
Setp 1 : Download NRTRpre_train/nrtr/latest.pth in mmocr_ijcai/nrtr/latest.pth
#Step 2
PORT=1234 ./tools/dist_train.sh configs/textrecog/nrtr/nrtr_tps++.py ./ckpt/ijcai_nrtr_tps_pp 4 --seed=123456 --load-from=mmocr_ijcai/nrtr/nrtr_latest.pth

Testing
Please refer to the testing configuration Testing Doc
Acknowledgement
This code is based on MMOCR
Citation
If you find our method useful for your reserach, please cite
@article{zheng2023tps++,
title={TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition},
author={Zheng, Tianlun and Chen, Zhineng and Bai, Jinfeng and Xie, Hongtao and Jiang, Yu-Gang},
journal={IJCAI},
year={2023}
}
License
This project is released under the Apache 2.0 license.
About
Official Pytorch implementations of TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition (IJCAI 2023）
Topics
ocrocr-recognitionpytorchtps
Resources
Readme
Apache-2.0 license
Activity
Stars
42 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Used by
Contributors
Languages