Datasets:
File size: 1,609 Bytes
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---
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>
```yaml
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
``` |