Update README.md

README.md

@@ -7,4 +7,30 @@ pipeline_tag: image-to-text
 
 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.' }]
+```
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/OORjA9b3gc5pvqJssq_9M.png)
+
+---
+
 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`).