This is an unofficial implementation of Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition
Official Torch implementation can be found here
Another PyTorch implementation can be found here
pip3 install -r requirements.txt
- Download the pretrained model from BaiduYun and unzip it.
- Run
python3 test.py --test_data_dir ./demo_data --checkpoints ./sar_synall_lmdb_checkpoints_2epochs -g "0" --vis_dir ./visualize
- Results will be printed and attention weights visualizing images can be found in directory './visualize'
- Data prepare
LMDB format is suggested. refer here to generate data in LMDB format. Also raw images with annoation file (json or txt) is also supported. The stucture of annoation file please refer to txt or json - Run
LMDB:Raw images:python3 train.py --checkpoints /path/to/save/checkpoints --train_data_dir /path/to/your/train/LMDB/data/dir --test_data_dir /path/to/your/test/LMDB/data/dir -g "0"More hyper-parameters please refer to config.pypython3 train.py --checkpoints /path/to/save/checkpoints --train_data_dir /path/to/your/train/images/dir --train_data_gt /path/to/your/train/annotation/file(txt or json) --test_data_dir /path/to/your/test/images/dir --test_data_gt /path/to/your/train/annotation/file(txt or json) -g "0"
Similar to demo and you can also provide annotation and it will calculate accuracy
python3 test.py --test_data_dir /path/to/your/test/images/dir --test_data_gt /path/to/your/test/annotation/file(optional) --checkpoints /path/to/trained/checkpoints -g "0"
If you want to create a predicion server, you can export the frozen model with this command
python3 freeze.py
By default it will take the last checkpoint in the ./checkpoints folder. To change it, use the --checkpoints parameter.
| IC13 | IC15 | SVTP | CUTE | |
|---|---|---|---|---|
| Official | 91.0 | 69.2 | 76.4 | 83.3 |
| This One | 91.8 | 69.6 | 75.1 | 83.6 |
