Burn implementation of CRAFT text detector | Paper | Pretrained Model | Supplementary
Youngmin Baek, Bado Lee, Dongyoon Han, Sangdoo Yun, Hwalsuk Lee.
Clova AI Research, NAVER Corp.
Adapted by Genna Wingert
Burn implementation for CRAFT text detector that effectively detect text area by exploring each character region and affinity between characters. The bounding box of texts are obtained by simply finding minimum bounding rectangles on binary map after thresholding character region and affinity scores.
Adapted from CRAFT-pytorch
Polygon processing is not yet implemented.
The code for training is not included in this repository, as the original authors cannot release the full training code for IP reason.
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Download the trained models (converted because originals use a legacy format)
Model name Used datasets Languages Purpose Model Link General SynthText, IC13, IC17 Eng + MLT For general purpose Click IC15 SynthText, IC15 Eng For IC15 only Click LinkRefiner CTW1500 - Used with the General Model Click -
Run with pretrained model
cargo run --example test-craft --release --trained_model=[weightfile] --test_image=[path to test image]The result image and socre maps will be saved to ./result by default.
--trained_model: pretrained model--text_threshold: text confidence threshold--low_text: text low-bound score--link_threshold: link confidence threshold--backend: backend to use for inference (default:wgpu)--max_size: max image size for inference--mag_ratio: image magnification ratio--test_file: file path to input image--refine: use link refiner for sentense-level dataset--refiner_model: pretrained refiner model
- Original implementation: https://github.com/clovaai/CRAFT-pytorch