README.md

---
license: apache-2.0
language:
- en
- zh
- ja
- vi
---

# InkSight Small-p 
From [InkSight: Offline-to-Online Handwriting Conversion by Learning to Read and Write](https://github.com/google-research/inksight)

<div style="display: flex; gap: 0.5rem; flex-wrap: wrap; margin-bottom: 1rem;">
<a href="https://research.google/blog/a-return-to-hand-written-notes-by-learning-to-read-write/">
 <img src="https://img.shields.io/badge/Google_Research_Blog-333333?&logo=google&logoColor=white" alt="Google Research Blog">
</a>
<a href="https://arxiv.org/abs/2402.05804">
 <img src="https://img.shields.io/badge/Read_the_Paper-4CAF50?&logo=arxiv&logoColor=white" alt="Read the Paper">
</a>
<a href="https://huggingface.co/spaces/Derendering/Model-Output-Playground">
 <img src="https://img.shields.io/badge/Output_Playground-007acc?&logo=huggingface&logoColor=white" alt="Try Demo on Hugging Face">
</a>
<a href="https://charlieleee.github.io/publication/inksight/">
 <img src="https://img.shields.io/badge/🔗_Project_Page-FFA500?&logo=link&logoColor=white" alt="Project Page">
</a>
<a href="https://huggingface.co/datasets/Derendering/InkSight-Derenderings">
 <img src="https://img.shields.io/badge/Dataset-InkSight-40AF40?&logo=huggingface&logoColor=white" alt="Hugging Face Dataset">
</a>
<a href="https://githubtocolab.com/google-research/inksight/blob/main/colab.ipynb">
 <img src="https://img.shields.io/badge/Example_Colab-F9AB00?&logo=googlecolab&logoColor=white" alt="Example colab">
</a>
</div>

<figure>
  <img src="https://charlieleee.github.io/publication/inksight/inksight_animation_gif.gif" alt="InkSight word-level" style="width: 100%;">
  <figcaption>The illustration on InkSight's word-level model outputs both text and digital ink through "Recognize and Derender" inference. </figcaption>
</figure>




<div style="font-size: 16px; margin-top: 20px;">
    <strong style="color: red;">Notice:</strong> Please use TensorFlow and tensorflow-text between version 2.15.0 and 2.17.0. Versions later than 2.17.0 may lead to unexpected behavior. We are currently investigating these issues.
</div>


## Example Usage

```python
from huggingface_hub import from_pretrained_keras
import tensorflow_text

model = from_pretrained_keras("Derendering/InkSight-Small-p")
cf = model.signatures['serving_default']

prompt = "Derender the ink." # "Recognize and derender." or "Derender the ink: <text>"

input_text = tf.constant([prompt], dtype=tf.string)
image_encoded = tf.reshape(tf.io.encode_jpeg(np.array(image)[:, :, :3]), (1, 1))
output = cf(**{'input_text': input_text, 'image/encoded': image_encoded})
```

<span>For full usage, please refer to the notebook: </span> <a href="https://githubtocolab.com/google-research/inksight/blob/main/colab.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" style="display: inline; vertical-align: middle;"></a>

## Model and Training Summary

<table style="width:100%; border-collapse: collapse; font-family: Arial, sans-serif;">
    <tr>
        <th style="width: 30%; border: 1px solid #333; padding: 10px; background-color: #f2f2f2;">Model Architecture</th>
        <td style="border: 1px solid #333; padding: 10px;">A multimodal sequence-to-sequence Transformer model with the mT5 encoder-decoder architecture. It takes text tokens and ViT dense image embeddings as inputs to an encoder and autoregressively predicts discrete text and ink tokens with a decoder.</td>
    </tr>
    <tr>
        <th style="width: 30%; border: 1px solid #333; padding: 10px; background-color: #f2f2f2;">Input(s)</th>
        <td style="border: 1px solid #333; padding: 10px;">A pair of image and text.</td>
    </tr>
    <tr>
        <th style="width: 30%; border: 1px solid #333; padding: 10px; background-color: #f2f2f2;">Output(s)</th>
        <td style="border: 1px solid #333; padding: 10px;">Generated digital ink and text.</td>
    </tr>
    <tr>
        <th style="width: 30%; border: 1px solid #333; padding: 10px; background-color: #f2f2f2;">Usage</th>
        <td style="border: 1px solid #333; padding: 10px;">
            <strong>Application:</strong> The model is for research prototype, and the public version is released and available for the public.<br>
            <strong>Known Caveats:</strong> None.
        </td>
    </tr>
    <tr>
        <th style="width: 30%; border: 1px solid #333; padding: 10px; background-color: #f2f2f2;">System Type</th>
        <td style="border: 1px solid #333; padding: 10px;">
            <strong>System Description:</strong> This is a standalone model.<br>
            <strong>Upstream Dependencies:</strong> None.<br>
            <strong>Downstream Dependencies:</strong> None.
        </td>
    </tr>
    <tr>
        <th style="width: 30%; border: 1px solid #333; padding: 10px; background-color: #f2f2f2;">Implementation Frameworks</th>
        <td style="border: 1px solid #333; padding: 10px;">
            <strong>Hardware & Software:</strong> Hardware: TPU v5e.<br>
            Software: T5X , JAX/Flax, Flaxformer.<br>
            <strong>Compute Requirements:</strong> We train all of our models for 340k steps with batch size 512. With frozen ViT encoders, the training of Small-i takes ∼33h on 64 TPU v5e chips and the training of Large-i takes ∼105h on 64 TPU v5e chips.
        </td>
    </tr>
    <tr>
        <th style="width: 30%; border: 1px solid #333; padding: 10px; background-color: #f2f2f2;">Data Overview</th>
        <td style="border: 1px solid #333; padding: 10px;">
            <strong>Training Datasets:</strong> The ViT encoder of Small-p is pretrained on ImageNet-21k, mT5 encoder and decoder are initialized from scratch. The entire model is trained on the mixture of publicly available datasets described in next section.
        </td>
    </tr>
    <tr>
        <th style="width: 30%; border: 1px solid #333; padding: 10px; background-color: #f2f2f2;">Evaluation Results</th>
        <td style="border: 1px solid #333; padding: 10px;">
            <strong>Evaluation Methods:</strong> Human evaluation (reported in Section 4.5.1 of the paper) and automated evaluations (reported in Section 4.5.2 of the paper).
        </td>
    </tr>
    <tr>
        <th style="width: 30%; border: 1px solid #333; padding: 10px; background-color: #f2f2f2;">Model Usage & Limitations</th>
        <td style="border: 1px solid #333; padding: 10px;">
            <strong>Sensitive Use:</strong> The model is capable of converting images to digital inks. This model should not be used for any of the privacy-intruding use cases, e.g., forging handwritings.<br>
            <strong>Known Limitations:</strong> Reported in Appendix I of the paper.<br>
            <strong>Ethical Considerations & Potential Societal Consequences:</strong> Reported in Sections 6.1 and 6.2 of the paper.
        </td>
    </tr>
</table>


## Citation

If you find our work useful for your research and applications, please cite using this BibTeX:
```bibtex
@article{mitrevski2024inksight,
  title={InkSight: Offline-to-Online Handwriting Conversion by Learning to Read and Write},
  author={Mitrevski, Blagoj and Rak, Arina and Schnitzler, Julian and Li, Chengkun and Maksai, Andrii and Berent, Jesse and Musat, Claudiu},
  journal={arXiv preprint arXiv:2402.05804},
  year={2024}
}
```