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End of preview. Expand in Data Studio
SynthDoG detection 🐕
OCR annotations with bounding boxes for synthdog-en generated with PaddleOcr.
This dataset contains annotations where the bounding boxes have the following formats:
2_coord: [(xmin, ymin), (xmax,ymax)]2_coord: [(xmin/w, ymin/h), (xmax/w,ymax/h)] normalized version of2_coordwhere (h, w) are the image height and width4_coord: [(x1, y1), (x2,y2), (x3,y3), (x4, y4)] all corners of the rectangle enclosing the text span4_coord_norm: [(x1/w, y1/h), (x2/w,y2/h), (x3/w,y3/h), (x4/w, y4/h)] normalized version of4_coord
Usage
from datasets import load_dataset
ds = load_dataset("nnethercott/synthdog-en-detection", split="train[:101]")
to visualize the boxes
from PIL import ImageDraw
sample = ds[-1]
img, boxes = sample['image'], sample['2_coord']
draw = ImageDraw.Draw(img)
for item in boxes:
draw.rectangle([tuple(xy) for xy in item['coord']], outline='red')
img.save('sample.jpg')
How to Cite
Always cite the original authors ! This dataset is just an annotated version of Clova AI's synthdog dataset. If you find this work useful to you, please cite them:
@inproceedings{kim2022donut,
title = {OCR-Free Document Understanding Transformer},
author = {Kim, Geewook and Hong, Teakgyu and Yim, Moonbin and Nam, JeongYeon and Park, Jinyoung and Yim, Jinyeong and Hwang, Wonseok and Yun, Sangdoo and Han, Dongyoon and Park, Seunghyun},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2022}
}
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