metadata

license: mit
dataset_info:
  features:
    - name: image
      dtype: image
    - name: text
      dtype: string
  splits:
    - name: train
      num_bytes: 921768062.125
      num_examples: 7703
    - name: train_numbers
      num_bytes: 15248375
      num_examples: 357
    - name: test
      num_bytes: 56689886.75
      num_examples: 3074
    - name: test_numbers
      num_bytes: 554420
      num_examples: 100
  download_size: 994154685
  dataset_size: 994260743.875
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: train_numbers
        path: data/train_numbers-*
      - split: test
        path: data/test-*
      - split: test_numbers
        path: data/test_numbers-*
task_categories:
  - image-to-text
language:
  - en
tags:
  - ocr
  - ctw1500
pretty_name: ctw1500

CTW1500

META

https://github.com/open-mmlab/mmocr/blob/main/dataset_zoo/ctw1500/metafile.yml

Name: 'CTW1500'
Paper:
  Title: Curved scene text detection via transverse and longitudinal sequence connection
  URL: https://www.sciencedirect.com/science/article/pii/S0031320319300664
  Venue: PR
  Year: '2019'
  BibTeX: '@article{liu2019curved,
  title={Curved scene text detection via transverse and longitudinal sequence connection},
  author={Liu, Yuliang and Jin, Lianwen and Zhang, Shuaitao and Luo, Canjie and Zhang, Sheng},
  journal={Pattern Recognition},
  volume={90},
  pages={337--345},
  year={2019},
  publisher={Elsevier}
}'
Data:
  Website: https://github.com/Yuliang-Liu/Curve-Text-Detector
  Language:
    - English
  Scene:
    - Scene
  Granularity:
    - Word
    - Line
  Tasks:
    - textrecog