README.md

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

<!-- Provide a quick summary of what the model is/does. -->


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->


<!-- 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]

<!-- Provide the basic links for the model. -->

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

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

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

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->


## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical 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

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**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