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README.md
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license: cc-by-nc-2.0
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---
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---
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license: cc-by-nc-2.0
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language:
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- en
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pipeline_tag: text spotting
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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<!-- Change names and language per model as needed -->
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- **Developed by:** Knowledge Computing Lab, University of Minnesota: Leeje Jang, Jina Kim, Zekun Li, Yijun Lin, Min Namgung, Yao-Yi Chiang
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- **Shared by:** Machines Reading Maps
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- **Model type:** text spotter
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- **Language(s):** English
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- **License:** CC-BY-NC 2.0
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** https://github.com/knowledge-computing/mapkurator-spotter
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- **Paper [optional]:** [More Information Needed]
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- **Documentation:** https://knowledge-computing.github.io/mapkurator-doc/#/
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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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.
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This version of the model was trained with an English language model.
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### Downstream Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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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.
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical 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.
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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.
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## How to Get Started with the Model
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Please refer to the mapKurator documentation for details: https://knowledge-computing.github.io/mapkurator-doc/#/
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## Training Details
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### Training Data
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<!-- 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. -->
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Synthetic training datasets:
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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.
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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.
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Model Card Authors
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Yijun Lin, Katherine McDonough, Valeria Vitale
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## Model Card Contact
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Yijun Lin, lin00786 at umn.edu
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