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README.md

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-# IterVM: Iterative Vision Modeling Module for Scene Text Recognition
-
-The official code of [IterNet](https://arxiv.org/abs/2204.02630).
-
-We propose IterVM, an iterative approach for visual feature extraction which can significantly improve scene text recognition accuracy.
-IterVM repeatedly uses the high-level visual feature extracted at the previous iteration to enhance the multi-level features extracted at the subsequent iteration.
-
-
-![framework](./figures/framework.png)
-
-
-## Runtime Environment
-```
-pip install -r requirements.txt
-```
-Note: `fastai==1.0.60` is required.
-
-## Datasets
-<details>
-  <summary>Training datasets (Click to expand) </summary>
-    1. [MJSynth](http://www.robots.ox.ac.uk/~vgg/data/text/) (MJ): 
-        - Use `tools/create_lmdb_dataset.py` to convert images into LMDB dataset
-        - [LMDB dataset BaiduNetdisk(passwd:n23k)](https://pan.baidu.com/s/1mgnTiyoR8f6Cm655rFI4HQ)
-    2. [SynthText](http://www.robots.ox.ac.uk/~vgg/data/scenetext/) (ST):
-        - Use `tools/crop_by_word_bb.py` to crop images from original [SynthText](http://www.robots.ox.ac.uk/~vgg/data/scenetext/) dataset, and convert images into LMDB dataset by `tools/create_lmdb_dataset.py`
-        - [LMDB dataset BaiduNetdisk(passwd:n23k)](https://pan.baidu.com/s/1mgnTiyoR8f6Cm655rFI4HQ)
-    3. [WikiText103](https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip), which is only used for pre-trainig language models:
-        - Use `notebooks/prepare_wikitext103.ipynb` to convert text into CSV format.
-        - [CSV dataset BaiduNetdisk(passwd:dk01)](https://pan.baidu.com/s/1yabtnPYDKqhBb_Ie9PGFXA)
-</details>
-
-<details>
-  <summary>Evaluation datasets (Click to expand) </summary>
-- Evaluation datasets, LMDB datasets can be downloaded from [BaiduNetdisk(passwd:1dbv)](https://pan.baidu.com/s/1RUg3Akwp7n8kZYJ55rU5LQ), [GoogleDrive](https://drive.google.com/file/d/1dTI0ipu14Q1uuK4s4z32DqbqF3dJPdkk/view?usp=sharing).
-    1. ICDAR 2013 (IC13)
-    2. ICDAR 2015 (IC15)
-    3. IIIT5K Words (IIIT)
-    4. Street View Text (SVT)
-    5. Street View Text-Perspective (SVTP)
-    6. CUTE80 (CUTE)
-</details>
-
-<details>
-  <summary>The structure of `data` directory (Click to expand) </summary>
-- The structure of `data` directory is
-    ```
-    data
-    ├── charset_36.txt
-    ├── evaluation
-    │   ├── CUTE80
-    │   ├── IC13_857
-    │   ├── IC15_1811
-    │   ├── IIIT5k_3000
-    │   ├── SVT
-    │   └── SVTP
-    ├── training
-    │   ├── MJ
-    │   │   ├── MJ_test
-    │   │   ├── MJ_train
-    │   │   └── MJ_valid
-    │   └── ST
-    ├── WikiText-103.csv
-    └── WikiText-103_eval_d1.csv
-    ```
-</details>
-
-## Pretrained Models
-
-Get the pretrained models from [GoogleDrive](https://drive.google.com/drive/folders/1C8NMI8Od8mQUMlsnkHNLkYj73kbAQ7Bl?usp=sharing). Performances of the pretrained models are summaried as follows:
-
-|Model|IC13|SVT|IIIT|IC15|SVTP|CUTE|AVG|
-|-|-|-|-|-|-|-|-|
-|IterNet|97.9|95.1|96.9|87.7|90.9|91.3|93.8|
-
-## Training
-
-1. Pre-train vision model
-    ```
-    CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py --config=configs/pretrain_vm.yaml
-    ```
-2. Pre-train language model
-    ```
-    CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --config=configs/pretrain_language_model.yaml
-    ```
-3. Train IterNet
-    ```
-    CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py --config=configs/train_iternet.yaml
-    ```
-Note:
-- You can set the `checkpoint` path for vision model (vm) and language model separately for specific pretrained model, or set to `None` to train from scratch
-
-
-## Evaluation
-
-```
-CUDA_VISIBLE_DEVICES=0 python main.py --config=configs/train_iternet.yaml --phase test --image_only
-```
-Additional flags:
-- `--checkpoint /path/to/checkpoint` set the path of evaluation model 
-- `--test_root /path/to/dataset` set the path of evaluation dataset
-- `--model_eval [alignment|vision]` which sub-model to evaluate
-- `--image_only` disable dumping visualization of attention masks
-
-## Run Demo
-[<a href="https://colab.research.google.com/drive/1XmZGJzFF95uafmARtJMudPLLKBO2eXLv?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/drive/1XmZGJzFF95uafmARtJMudPLLKBO2eXLv?usp=sharing)
-
-```
-python demo.py --config=configs/train_iternet.yaml --input=figures/demo
-```
-Additional flags:
-- `--config /path/to/config` set the path of configuration file 
-- `--input /path/to/image-directory` set the path of image directory or wildcard path, e.g, `--input='figs/test/*.png'`
-- `--checkpoint /path/to/checkpoint` set the path of trained model
-- `--cuda [-1|0|1|2|3...]` set the cuda id, by default -1 is set and stands for cpu
-- `--model_eval [alignment|vision]` which sub-model to use
-- `--image_only` disable dumping visualization of attention masks
-
-
-## Citation
-If you find our method useful for your reserach, please cite
-```bash 
-@article{chu2022itervm,
-  title={IterVM: Iterative Vision Modeling Module for Scene Text Recognition},
-  author={Chu, Xiaojie and Wang, Yongtao},
-  journal={arXiv preprint arXiv:2204.02630},
-  year={2022}
-}
- ```
-
-## License
-The project is only free for academic research purposes, but needs authorization for commerce. For commerce permission, please contact wyt@pku.edu.cn.
-
-## Acknowledgements
-This project is based on [ABINet](https://github.com/FangShancheng/ABINet.git).
-Thanks for their great works.
-
+---
+title: Pixelplanet OCR
+emoji: 🏃
+colorFrom: indigo
+colorTo: red
+sdk: gradio
+sdk_version: 2.8.12
+app_file: app.py
+pinned: false
+license: bsd
+---