CharNet:

python tools/test_net.py configs/icdar2015_hourglass88.yaml <images_dir> <results_dir>

deep-text-recognition

CUDA_VISIBLE_DEVICES=0 python3 demo.py
--Transformation TPS --FeatureExtraction ResNet --SequenceModeling BiLSTM --Prediction Attn
--image_folder demo_image/
--saved_model TPS-ResNet-BiLSTM-Attn.pth

Arguments

--train_data: folder path to training lmdb dataset. --valid_data: folder path to validation lmdb dataset. --eval_data: folder path to evaluation (with test.py) lmdb dataset. --select_data: select training data. default is MJ-ST, which means MJ and ST used as training data. --batch_ratio: assign ratio for each selected data in the batch. default is 0.5-0.5, which means 50% of the batch is filled with MJ and the other 50% of the batch is filled ST. --data_filtering_off: skip data filtering when creating LmdbDataset. --Transformation: select Transformation module [None | TPS]. --FeatureExtraction: select FeatureExtraction module [VGG | RCNN | ResNet]. --SequenceModeling: select SequenceModeling module [None | BiLSTM]. --Prediction: select Prediction module [CTC | Attn]. --saved_model: assign saved model to evaluation. --benchmark_all_eval: evaluate with 10 evaluation dataset versions, same with Table 1 in our paper.

CRAFT Text Detection

pip install -r requirements.txt python test.py --trained_model=[weightfile] --test_folder=[folder path to test images]

Arguments

--trained_model: pretrained model --text_threshold: text confidence threshold --low_text: text low-bound score --link_threshold: link confidence threshold --cuda: use cuda for inference (default:True) --canvas_size: max image size for inference --mag_ratio: image magnification ratio --poly: enable polygon type result --show_time: show processing time --test_folder: folder path to input images --refine: use link refiner for sentense-level dataset --refiner_model: pretrained refiner model