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--- |
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tags: |
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- trocr |
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library_name: transformers.js |
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pipeline_tag: image-to-text |
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--- |
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https://huggingface.co/microsoft/trocr-base-handwritten with ONNX weights to be compatible with Transformers.js. |
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## Usage (Transformers.js) |
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If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using: |
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```bash |
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npm i @xenova/transformers |
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``` |
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**Example:** Optical character recognition w/ `Xenova/trocr-base-handwritten`. |
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```js |
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import { pipeline } from '@xenova/transformers'; |
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// Create image-to-text pipeline |
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const captioner = await pipeline('image-to-text', 'Xenova/trocr-base-handwritten'); |
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// Perform optical character recognition |
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const image = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/handwriting.jpg'; |
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const output = await captioner(image); |
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// [{ generated_text: 'Mr. Brown commented icily.' }] |
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``` |
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--- |
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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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). |
