https://huggingface.co/vikp/texify with ONNX weights to be compatible with Transformers.js.
Usage (Transformers.js)
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @xenova/transformers
Example: Image-to-text w/ Xenova/texify.
import { pipeline } from '@xenova/transformers';
// Create an image-to-text pipeline
const texify = await pipeline('image-to-text', 'Xenova/texify');
// Generate LaTeX from image
const image = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/latex2.png';
const latex = await texify(image, { max_new_tokens: 384 });
console.log(latex);
// [{ generated_text: "$$ |\\ \\frac{1}{x}=\\frac{1}{c}|=|\\ \\frac{c-x}{xc}|=\\frac{1}{|x|}\\cdot\\frac{1}{|c|}\\cdot|x-c|$$\n\nThe factor $$ \\frac{1}{|x|}$$ is not good if its near 0." }]
| Input image | Visualized output |
|---|---|
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Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).
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