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README.md

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-# Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition
-
-The official code of [ABINet](https://arxiv.org/pdf/2103.06495.pdf) (CVPR 2021, Oral).
-
-ABINet uses a vision model and an explicit language model to recognize text in the wild, which are trained in end-to-end way. The language model (BCN) achieves bidirectional language representation in simulating cloze test, additionally utilizing iterative correction strategy.
-
-![framework](./figs/framework.png)
-
-## Runtime Environment
-
-- We provide a pre-built docker image using the Dockerfile from `docker/Dockerfile`
-
-- Running in Docker
-    ```
-    $ git@github.com:FangShancheng/ABINet.git
-    $ docker run --gpus all --rm -ti --ipc=host -v $(pwd)/ABINet:/app fangshancheng/fastai:torch1.1 /bin/bash
-    ```
-- (Untested) Or using the dependencies
-    ```
-    pip install -r requirements.txt
-    ```
-
-## Datasets
-
-- Training datasets
-
-    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)
-
-- 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)
-
-
-- 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
-    ```
-
-### Pretrained Models
-
-Get the pretrained models from [BaiduNetdisk(passwd:kwck)](https://pan.baidu.com/s/1b3vyvPwvh_75FkPlp87czQ), [GoogleDrive](https://drive.google.com/file/d/1mYM_26qHUom_5NU7iutHneB_KHlLjL5y/view?usp=sharing). Performances of the pretrained models are summaried as follows:
-
-|Model|IC13|SVT|IIIT|IC15|SVTP|CUTE|AVG|
-|-|-|-|-|-|-|-|-|
-|ABINet-SV|97.1|92.7|95.2|84.0|86.7|88.5|91.4|
-|ABINet-LV|97.0|93.4|96.4|85.9|89.5|89.2|92.7|
-
-## Training
-
-1. Pre-train vision model
-    ```
-    CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --config=configs/pretrain_vision_model.yaml
-    ```
-2. Pre-train language model
-    ```
-    CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --config=configs/pretrain_language_model.yaml
-    ```
-3. Train ABINet
-    ```
-    CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --config=configs/train_abinet.yaml
-    ```
-Note:
-- You can set the `checkpoint` path for vision and language models separately for specific pretrained model, or set to `None` to train from scratch
-
-
-## Evaluation
-
-```
-CUDA_VISIBLE_DEVICES=0 python main.py --config=configs/train_abinet.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
-
-```
-python demo.py --config=configs/train_abinet.yaml --input=figs/test
-```
-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
-
-## Visualization
-Successful and failure cases on low-quality images:
-
-![cases](./figs/cases.png)
-
-## Citation
-If you find our method useful for your reserach, please cite
-```bash 
-@article{fang2021read,
-  title={Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition},
-  author={Fang, Shancheng and Xie, Hongtao and Wang, Yuxin and Mao, Zhendong and Zhang, Yongdong},
-    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-  year={2021}
-}
- ```
-
- ## License
-
-This project is only free for academic research purposes, licensed under the 2-clause BSD License - see the LICENSE file for details.
-
-Feel free to contact fangsc@ustc.edu.cn if you have any questions.
+---
+title: ABINet OCR
+emoji: 🏃
+colorFrom: indigo
+colorTo: red
+sdk: gradio
+sdk_version: 2.8.12
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference