- Reproduce weak-supervision training as mentioned in the paper https://arxiv.org/pdf/1904.01941.pdf
- Generate character bbox on all the popular data sets.
- Expose pre-trained models with command line interface to synthesize results on custom images
git clone https://github.com/autonise/CRAFT-Remade.git
cd CRAFT-Remade
conda env create -f environment.yml
conda activate craft
pip install -r requirements.txt
Put the images inside a folder.
Get a pre-trained model from the pre-trained model list (Currently only strong supervision using SYNTH-Text available)
Run the command -
python main.py synthesize --model=./model/final_model.pkl --folder=./input
| Dataset | Recall | Precision | F-score |
|---|---|---|---|
| ICDAR2013 | TBD | TBD | 0.8201(Improving) |
| ICDAR2015 | TBD | TBD | TBD(Coming Soon) |
| ICDAR2017 | TBD | TBD | TBD(Coming Soon) |
| Total Text | TBD | TBD | TBD(Coming Soon) |
| MS COCO | TBD | TBD | TBD(Coming Soon) |
SynthText(CRAFT Model) - https://drive.google.com/open?id=1QH0B-iQ1Ob2HkWCQ2bVCsLPwVSmbcSgN
SynthText(ResNet-UNet Model) - https://drive.google.com/file/d/1qnLM_iMnR1P_6OLoUoFtrReHe4bpFW3T
Original Model by authors - https://drive.google.com/open?id=1ZQE0tK9498RhLcXwYRgod4upmrYWdgl9
- ICDAR 2013 - https://drive.google.com/file/d/16dl08NtuXX0_8N-vi6uNqQHe83eIvy3U/view?usp=sharing
- ICDAR 2015 - In Progress
- ICDAR 2017 - In Progress
- Total Text - yet_to_be_completed
- MS-COCO - yet_to_be_completed
- ICDAR 2013 - In Progress
- ICDAR 2015 - In Progress
- ICDAR 2017 - yet_to_be_completed
- Total Text - yet_to_be_completed
- MS-COCO - yet_to_be_completed
Download the pre-trained model on Synthetic dataset at https://drive.google.com/open?id=1qnLM_iMnR1P_6OLoUoFtrReHe4bpFW3T
Otherwise if you want to train from scratch
Run the command -
python main.py train_synth
To test your model on SynthText, Run the command -
python main.py test_synth --model /path/to/model
*Currently Supported - [IC13, IC15]
The assumed structure of the dataset is
.
├── Generated (This folder will contain the weak-supervision intermediate targets)
└── Images
├── test
│ ├── img_1.jpg
│ ├── img_2.jpg
│ ├── img_3.jpg
│ ├── img_4.jpg
│ └── img_5.jpg
│ └── ...
├── test_gt.json (This can be generated using the pre_process function described below)
├── train
│ ├── img_1.jpg
│ ├── img_2.jpg
│ ├── img_3.jpg
│ ├── img_4.jpg
│ └── img_5.jpg
│ └── ...
└── train_gt.json (This can be generated using the pre_process function described below)
To generate the json files for IC13 -
In config.py change the corresponding values
'ic13': {
'train': {
'target_json_path': None, --> path to where you want the target json file (Images/train_gt.json)
'target_folder_path': None, --> path to where you downloaded the train gt (ch2_training_localization_transcription_gt)
},
'test': {
'target_json_path': None, --> path to where you want the target json file (Images/test_gt.json)
'target_folder_path': None, --> path to where you downloaded the train gt (Challenge2_Test_Task1_GT)
}
Run the command -
python main.py pre_process --dataset IC13
To generate the json files for IC15 -
In config.py change the corresponding values
'ic15': {
'train': {
'target_json_path': None, --> path to where you want the target json file (Images/train_gt.json)
'target_folder_path': None, --> path to where you downloaded the train gt (ch4_training_localization_transcription_gt)
},
'test': {
'target_json_path': None, --> path to where you want the target json file (Images/test_gt.json)
'target_folder_path': None, --> path to where you downloaded the train gt (Challenge4_Test_Task1_GT)
}
Run the command - python main.py pre_process --dataset IC15
Run the command -
python main.py weak_supervision --model /path/to/strong/supervision/model --iterations <num_of_iterations(20)>
This will train the weak supervision model for the number of iterations you specified