| base_model: microsoft/trocr-base-handwritten | |
| library_name: transformers.js | |
| pipeline_tag: image-to-text | |
| tags: | |
| - trocr | |
| 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](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using: | |
| ```bash | |
| npm i @xenova/transformers | |
| ``` | |
| **Example:** Optical character recognition w/ `Xenova/trocr-base-handwritten`. | |
| ```js | |
| 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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). |