CCPD-Green
2D Polygon
OCR/Text Detection
|...
许可协议: MIT

Overview

[ECCV 2018] CCPD: a diverse and well-annotated dataset for license plate detection and recognition

Data Annotation

Annotations are embedded in file name.

A sample image name is "025-95_113-154&383_386&473-386&473_177&454_154&383_363&402-0_0_22_27_27_33_16-37-15.jpg". Each name can be splited into seven fields. Those fields are explained as follows.

  • Area: Area ratio of license plate area to the entire picture area.
  • Tilt degree: Horizontal tilt degree and vertical tilt degree.
  • Bounding box coordinates: The coordinates of the left-up and the right-bottom vertices.
  • Four vertices locations: The exact (x, y) coordinates of the four vertices of LP in the whole image. These coordinates start from the right-bottom vertex.
  • License plate number: Each image in CCPD has only one LP. Each LP number is comprised of a Chinese character, a letter, and five letters or numbers. A valid Chinese license plate consists of seven characters: province (1 character), alphabets (1 character), alphabets+digits (5 characters). "0_0_22_27_27_33_16" is the index of each character. These three arrays are defined as follows. The last character of each array is letter O rather than a digit 0. We use O as a sign of "no character" because there is no O in Chinese license plate characters.
provinces = ["皖", "沪", "津", "渝", "冀", "晋",
"蒙", "辽", "吉", "黑", "苏", "浙", "京", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", "琼", "川", "贵", "云",
"藏", "陕", "甘", "青", "宁", "新", "警", "学", "O"]
alphabets = ['A', 'B', 'C', 'D', 'E', 'F', 'G',
'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W',
             'X', 'Y', 'Z', 'O']
ads = ['A', 'B', 'C',
'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X',
       'Y', 'Z', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'O']
  • Brightness: The brightness of the license plate region.
  • Blurriness: The Blurriness of the license plate region.

Citation

@inproceedings{xu2018towards,
  title={Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline},
  author={Xu, Zhenbo and Yang, Wei and Meng, Ajin and Lu, Nanxue and Huang, Huan},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={255--271},
  year={2018}
}

License

MIT

数据概要
数据格式
Image,
数据量
300K
文件大小
865.66MB
发布方
University of Science and Technology of China
The University of science and technology of China (USTC) is a comprehensive national key university affiliated to the Chinese Academy of Sciences, which focuses on cutting-edge science and high-tech, and combines medicine, characteristic management and humanities.
数据集反馈
| 433 | 数据量 300K | 大小 865.66MB
CCPD-Green
2D Polygon
OCR/Text Detection
许可协议: MIT

Overview

[ECCV 2018] CCPD: a diverse and well-annotated dataset for license plate detection and recognition

Data Annotation

Annotations are embedded in file name.

A sample image name is "025-95_113-154&383_386&473-386&473_177&454_154&383_363&402-0_0_22_27_27_33_16-37-15.jpg". Each name can be splited into seven fields. Those fields are explained as follows.

  • Area: Area ratio of license plate area to the entire picture area.
  • Tilt degree: Horizontal tilt degree and vertical tilt degree.
  • Bounding box coordinates: The coordinates of the left-up and the right-bottom vertices.
  • Four vertices locations: The exact (x, y) coordinates of the four vertices of LP in the whole image. These coordinates start from the right-bottom vertex.
  • License plate number: Each image in CCPD has only one LP. Each LP number is comprised of a Chinese character, a letter, and five letters or numbers. A valid Chinese license plate consists of seven characters: province (1 character), alphabets (1 character), alphabets+digits (5 characters). "0_0_22_27_27_33_16" is the index of each character. These three arrays are defined as follows. The last character of each array is letter O rather than a digit 0. We use O as a sign of "no character" because there is no O in Chinese license plate characters.
provinces = ["皖", "沪", "津", "渝", "冀", "晋",
"蒙", "辽", "吉", "黑", "苏", "浙", "京", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", "琼", "川", "贵", "云",
"藏", "陕", "甘", "青", "宁", "新", "警", "学", "O"]
alphabets = ['A', 'B', 'C', 'D', 'E', 'F', 'G',
'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W',
             'X', 'Y', 'Z', 'O']
ads = ['A', 'B', 'C',
'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X',
       'Y', 'Z', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'O']
  • Brightness: The brightness of the license plate region.
  • Blurriness: The Blurriness of the license plate region.

Citation

@inproceedings{xu2018towards,
  title={Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline},
  author={Xu, Zhenbo and Yang, Wei and Meng, Ajin and Lu, Nanxue and Huang, Huan},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={255--271},
  year={2018}
}

License

MIT

数据集反馈
0
立即开始构建AI
graviti
wechat-QR
长按保存识别二维码,关注Graviti公众号