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https://huggingface.co/microsoft/trocr-base-handwritten 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: Optical character recognition w/ Xenova/trocr-base-handwritten.

import { pipeline } from '@xenova/transformers';

// Create image-to-text pipeline
const captioner = await pipeline('image-to-text', 'Xenova/trocr-base-handwritten');

// Perform optical character recognition
const image = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/handwriting.jpg';
const output = await captioner(image);
// [{ generated_text: 'Mr. Brown commented icily.' }]


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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