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The official code of CDistNet.
Modellquelle
Quellenbeschreibung
The official code of CDistNet.
Paper Link : Arxiv Link
Quellen
1 QuelleVerifiziert 10. Sept.
Modellartefakte
1 Artefaktmodels/all_tiny_13font_persian_cdistnet_128_36/epoch7_best_acc.pth
pth · 93,5 MB · SHA-256 9b3b4e0857f0…bf19 · Hugging Face
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we test other sota method in HA-IC13 and CA-IC13 datasets.
HA_CA CDistNet has a performance advantage over other SOTA methods as the character distance increases (1-6)
| Method | 1 | 2 | 3 | 4 | 5 | 6 | Code & Pretrain model |
|---|---|---|---|---|---|---|---|
| VisionLAN (ICCV 2021) | 93.58 | 92.88 | 89.97 | 82.26 | 72.23 | 61.03 | Offical Code |
| ABINet (CVPR 2021 ) | 95.92 | 95.22 | 91.95 | 85.76 | 73.75 | 64.99 | Offical Code |
| RobustScanner* (ECCV 2020) | 96.15 | 95.33 | 93.23 | 88.91 | 81.10 | 71.53 | -- |
| Transformer-baseline* | 96.27 |
| Method | 1 | 2 | 3 | 4 | 5 | 6 | Code & Pretrain model |
|---|---|---|---|---|---|---|---|
| VisionLAN (ICCV 2021) | 94.87 | 92.77 | 84.01 | 75.03 | 64.29 | 52.74 | Offical Code |
| ABINet (CVPR 2021 ) | 96.62 | 95.92 | 87.86 | 76.31 | 65.46 | 54.49 | Offical Code |
| RobustScanner* (ECCV 2020) | 95.22 | 94.87 | 85.30 | 76.55 | 68.38 | 60.79 | -- |
| Transformer-baseline* |
The datasets are same as ABINet
Training datasets
Evaluation & Test datasets, LMDB datasets can be downloaded from BaiduNetdisk(passwd:1dbv), GoogleDrive.
Augment IC13
The structure of dataset directory is
dataset
├── eval
│ ├── CUTE80
│ ├── IC13_857
│ ├── IC15_1811
│ ├── IIIT5k_3000
│ ├── SVT
│ └── SVTP
├── train
│ ├── MJ
│ │ ├── MJ_test
│ │ ├── MJ_train
│ │ └── MJ_valid
│ └── ST
package you can find in env_cdistnet.yaml.
#Installed
conda create -n CDistNet python=3.7
conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=9.2 -c pytorch
pip install opencv-python mmcv notebook numpy einops tensorboardX Pillow thop timm tornado tqdm matplotlib lmdb
Get the pretrained models from BaiduNetdisk(passwd:d6jd), GoogleDrive.
(We both offer training log and result.csv in same file.)
The pretrained model should set in models/reconstruct_CDistNetv3_3_10
Performances of the pretrained models are summaried as follows:
comment: <> (|CDistNet(paper)|6|97.67|93.82|96.57|86.25|89.77|89.58|92.28|)
comment: <> (|CDistNet(rebuild)|4|97.43|93.51|96.37|86.03|88.68|93.4|92.57|)
CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --config=configs/CDistNet_config.py
CUDA_VISIBLE_DEVICES=0 python eval.py --config=configs/CDistNet_config.py
@article{Zheng2021CDistNetPM,
title={CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition},
author={Tianlun Zheng and Zhineng Chen and Shancheng Fang and Hongtao Xie and Yu-Gang Jiang},
journal={ArXiv},
year={2021},
volume={abs/2111.11011}
}
--- datasets: - mohammadsaleh40/persian-word-ocr language: - fa pipeline_tag: image-to-text --- # CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition The official code of CDistNet. Paper Link : [Arxiv Link](http://arxiv.org/abs/2111.11011) ## What's News - [2023-08]🌟 Our paper is accepted by IJCV - [2022-01]🌟 Our code is released in github - [2021-11]🌟 The paper can be read in Arixv: http://arxiv.org/abs/2111.11011  ## To Do List * [x] HA-IC13 & CA-IC13 * [x] Pre-train model * [x] Cleaned Code * [ ] Document * [ ] Distributed Training ## Two New Datasets we test other sota method in HA-IC13 and CA-IC13 datasets.  **CDistNet has a performance advantage over other SOTA methods as the character distance increases (1-6)** ### HA-IC13 |Method |1 | 2 | 3 | 4 | 5 | 6 | Code & Pretrain model| |- | - | - | - | - | - | - | - | |VisionLAN (ICCV 2021) | 93.58 | 92.88 | 89.97 | 82.26 | 72.23 | 61.03 | [Offical Code](https://github.com/wangyuxin87/VisionLAN)| |ABINet (CVPR 2021 ) | 95.92 |95.22 | 91.95 | 85.76 | 73.75 | 64.99 | [Offical Code](https://github.com/FangShancheng/ABINet)| |RobustScanner* (ECCV 2020) | 96.15 | 95.33 | 93.23 | 88.91 | 81.10 |71.53 | -- | | Transformer-baseline* | 96.27 | 95.45 | 92.42 | 86.46 | 79.35 | 72.46 | -- | |CDistNet |**96.62**| **96.15** | **94.28** | **89.96** | **83.43** | **77.71** | -- | ### CA-IC13 |Method |1 | 2 | 3 | 4 | 5 | 6 | Code & Pretrain model| |- | - | - | - | - | - | - | - | |VisionLAN (ICCV 2021) | 94.87 | 92.77 | 84.01 | 75.03 | 64.29 | 52.74 | [Offical Code](https://github.com/wangyuxin87/VisionLAN)| |ABINet (CVPR 2021 ) | **96.62** | **95.92** | 87.86 |76.31 | 65.46 | 54.49 | [Offical Code](https://github.com/FangShancheng/ABINet)| |RobustScanner* (ECCV 2020) | 95.22 | 94.87 | 85.30 | 76.55 | 68.38 |60.79 | -- | | Transformer-baseline* | 95.68 | 94.40 | 85.88 | 75.85 | 65.93 | 58.58 | -- | |CDistNet | 96.27 | 95.57 | **88.45** | **79.58** | **70.36** | **63.13** | -- | ## Datasets **The datasets are same as ABINet** - Training datasets 1. [MJSynth](http://www.robots.ox.ac.uk/~vgg/data/text/) (MJ): - [LMDB dataset BaiduNetdisk(passwd:n23k)](https://pan.baidu.com/s/1mgnTiyoR8f6Cm655rFI4HQ) 2. [SynthText](http://www.robots.ox.ac.uk/~vgg/data/scenetext/) (ST): - [LMDB dataset BaiduNetdisk(passwd:n23k)](https://pa...
Source context: 0 downloads · 0 likes · Pipeline image-to-text · Repo mohammadsaleh40/persian-cdistnet-ocr
| 95.45 |
| 92.42 |
| 86.46 |
| 79.35 |
| 72.46 |
| -- |
| CDistNet | 96.62 | 96.15 | 94.28 | 89.96 | 83.43 | 77.71 | -- |
| 95.68 |
| 94.40 |
| 85.88 |
| 75.85 |
| 65.93 |
| 58.58 |
| -- |
| CDistNet | 96.27 | 95.57 | 88.45 | 79.58 | 70.36 | 63.13 | -- |