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RoadText-1K
2D Box Tracking
Autonomous Driving
|...
License: 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}
}
Data Summary
Type
Video,
Amount
--
Size
--
Provided by
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.
| Amount -- | Size --
RoadText-1K
2D Box Tracking
Autonomous Driving
License: 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}
}
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