text-spotting scene-text-detection maps cultural-heritage pytorch image-to-text en license:cc-by-nc-2.0 region:us
---
license: cc-by-nc-2.0
language:
- en
tags:
- text-spotting
- scene-text-detection
- maps
- cultural-heritage
- pytorch
- image-to-text
---
# Model Card for Model ID

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## Model Details

### Model Description

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<!-- Change names and language per model as needed -->
- **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]

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- **Repository:** https://github.com/knowledge-computing/mapkurator-spotter
- **Paper [optional]:** [More Information Needed]
- **Documentation:** https://knowledge-computing.github.io/mapkurator-doc/#/

## Uses

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### Direct Use

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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

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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

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## Bias, Risks, and Limitations

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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

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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

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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]

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**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