Paper | Pretrained Model | Supplementary
Youngmin Baek, Bado Lee, Dongyoon Han, Sangdoo Yun, Hwalsuk Lee.
Clova AI Research, NAVER Corp.
from inference_utils import load_image, load_detector, test_net, visualize_detection
checkpoint_path = 'weights/craft_mlt_25k.pth'
device = 'cuda'
net = load_detector(checkpoint_path, device)
image_size = 1280 # image size for inference
mag_ratio = 1.5 # image magnification ratio
poly = False # enable polygon type
text_threshold = 0.7 # text confidence threshold
low_text = 0.4 # text low-bound score
link_threshold = 0.4 # link confidence threshold
image = load_image('/home/vladislav.leketush/Documents/chester_minsk_20.07.2019.jpg')
bboxes, polys, score_text = test_net(net, image, image_size, mag_ratio, text_threshold, link_threshold, low_text, device, poly)
visualize_detection(image, bboxes)
PyTorch 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.
@article{baek2019character,
title={Character Region Awareness for Text Detection},
author={Baek, Youngmin and Lee, Bado and Han, Dongyoon and Yun, Sangdoo and Lee, Hwalsuk},
journal={arXiv preprint arXiv:1904.01941},
year={2019}
}
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