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Spotter-v2-english

Image-to-Text
PyTorch
English
text-spotting
scene-text-detection
maps
cultural-heritage
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metadata
license: cc-by-nc-2.0
language:
  - en
tags:
  - text spotting
  - scene text detection
  - maps
  - cultural heritage
  - pytorch

Model Card for Model ID

Model Details

Model Description

  • Developed by: Knowledge Computing Lab, University of Minnesota: Leeje Jang, Jina Kim, Zekun Li, Yijun Lin, Min Namgung, Yao-Yi Chiang
  • Shared by: Machines Reading Maps
  • Model type: text spotter
  • Language(s): English
  • License: CC-BY-NC 2.0

Model Sources [optional]

Uses

Direct Use

The model detects and recognizes text on images. It was trained specifically to identify text on a wide range of historical maps with many styles printed between ca. 1500-2000 provided by the David Rumsey Map Collection. This version of the model was trained with an English language model.

Downstream Use

Using this model for new experiments will require attention to the style and language of text on images, including (possibly) the creation of new, synthetic or other training data.

Out-of-Scope Use

Bias, Risks, and Limitations

This model will struggle to return high quality results for maps with complex fonts, low contrast images, complex background colors and textures, and non-English language words.

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Please refer to the mapKurator documentation for details: https://knowledge-computing.github.io/mapkurator-doc/#/

Training Details

Training Data

Synthetic training datasets:

  1. SynthText: 40k text-free background images from COCO and use them to generate synthetic text images (see the left image). Code: https://github.com/ankush-me/SynthText; Dataset: TBD.
  2. SynMap: "patches" of synthetic maps that mimic the text (e.g., font, spacing, orientation) and background styles in the real historical maps (see the right image). Code: TBD; Dataset: TBD.

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Model Card Authors

Yijun Lin, Katherine McDonough, Valeria Vitale

Model Card Contact

Yijun Lin, lin00786 at umn.edu