graviti
产品服务
解决方案
知识库
公开数据集
关于我们
RoadText-1K
2D Box
2D Box Tracking
Autonomous Driving
|...
许可协议: Unknown

Overview

Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new "RoadText-1K" dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection, recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving.

Citation

Please use the following citation when referencing the dataset:

@article{reddy2020roadtext,
  title={RoadText-1K: Text Detection \& Recognition Dataset for Driving Videos},
  author={Reddy, Sangeeth and Mathew, Minesh and Gomez, Lluis and Rusinol, Mar{\c{c}}al and Jawahar, CV and others},
  journal={arXiv preprint arXiv:2005.09496},
  year={2020}
}
数据概要
数据格式
text, video, image,
数据量
--
文件大小
--
发布方
CVIT(Centre for Visual Information Technology)
CVIT focuses on basic and advanced research in image processing, computer vision, computer graphics and machine learning. This center deals with the generation, processing, and understanding of primarily visual data as well as with the techniques and tools required doing so efficiently. The activity of this center overlaps the traditional areas of Computer Vision, Image Processing, Computer Graphics, Pattern Recognition and Machine Learning.
| 数据量 -- | 大小 --
RoadText-1K
2D Box 2D Box Tracking
Autonomous Driving
许可协议: Unknown

Overview

Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new "RoadText-1K" dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection, recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving.

Citation

Please use the following citation when referencing the dataset:

@article{reddy2020roadtext,
  title={RoadText-1K: Text Detection \& Recognition Dataset for Driving Videos},
  author={Reddy, Sangeeth and Mathew, Minesh and Gomez, Lluis and Rusinol, Mar{\c{c}}al and Jawahar, CV and others},
  journal={arXiv preprint arXiv:2005.09496},
  year={2020}
}
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

Copyright@Graviti
沪ICP备19019574号
沪公网安备 31011002004865号