2D Box Tracking
Autonomous Driving
许可协议: Unknown


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.


Please use the following citation when referencing the dataset:

  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},
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.