Published July 22, 2024 | Version v1
Dataset Open

MediaText: a media industry-based dataset for scene text detetcion

  • 1. ROR icon Silesian University of Technology
  • 2. media-press.tv group
  • 3. ROR icon Yale University
  • 4. ROR icon Yale Cancer Center
  • 5. ROR icon Yale School of Medicine

Description

Media-Text

Media-Text dataset comprising images of banners, posters, covers and another images characterised for media industry.

Full paper is available here: Media-Text: a Media Industry-Based Dataset for Scene Text Detection

DATASET DESCRIPTION

  • 400 images
  • 7 744 annotated text instances
  • 973 annotations have been marked as illegible for the task of text recognition
  • 659 texts have been markes as do not care (###) for scene text detection.
  • Images are represented by 193 unique resolutions.

Annotation Format - Each image has corresponding  gt_*.txt file, which contains annotations in bounding box format (defined by 4 courners), transcription, and bool flag which determines that text is illegible for OCR. Proposed format is similar to ICDAR15 annotations.

x1, x2, ..., x4, y4, transcription, OCR Flag 

Example:

37,68,198,49,214,181,52,200,LADIES,False

ACKNOWLEDGMENT

This work was supported by the Silesian University of Technology (SUT) through the subsidy for maintaining and developing research potential grant in 2024 for young researchers, No. 2/070/BKM24/0058, and by the Ministry of Science and Higher Education "Implementation Doctorate" No. DWD/5/0511/2021.

Thanks to the graphic department of media-press group for the preparation and possibility of sharing graphics thematically related to the prepared dataset.

 

LICENSE

Annotations created by authors are licesned under CC-BY-4.0 license.Images from the Open-Image-V7 dataset and are licensed according to their source information. Source information is defined in a file metadata.csv file that defines all the metadata of each file (File name corresponds to the ImageID column).

Images whose name corresponds to the media_press pattern are provided for academic use.

CITING THE RELATED WORKS
 
Please cite the related works in your publications if it helps your research:

```
@inproceedings{inproceedings,
author = {Kalisz, Seweryn and Marczyk, Michał and Polanska, Joanna},
booktitle = {Modelling and simulation 2024. The 2024 European Simulation and Modelling Conference}
editor = {Manuel Graña; J. David Nuñez-Gonzalez}
year = {2024},
month = {10},
pages = {138-144},
publisher = {EUROSIS-ETI},
title = {Media-Text: a Media Industry-Based Dataset for Scene Text Detection}
}
```

Files

annotations.zip

Files (142.0 MB)

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md5:7c9a0b0cdee00aaafad930f615e5ce1b
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md5:c06790306650108d1fa79e2619e1bc82
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md5:b09825b4c875fd4a68972eb298e7a75b
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Additional details

Funding

Silesian University of Technology
Silesian University of Technology (SUT) through the subsidy for maintaining and developing research potential grant in 2024 for young researchers 02/070/BKM24/0058
Ministry of Science and Higher Education
Implementation Doctorate DWD/5/0511/2021

Software

Repository URL
https://github.com/ZAEDPolSl/MediaText
Programming language
Python
Development Status
Active