Update README.md

README.md

@@ -5,4 +5,37 @@ base_model: alibaba-damo/mgp-str-base
 
 https://huggingface.co/alibaba-damo/mgp-str-base 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/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+
+**Example:** Optical Character Recognition (OCR) w/ `onnx-community/mgp-str-base`
+
+```js
+import { MgpstrForSceneTextRecognition, MgpstrProcessor, RawImage } from '@huggingface/transformers';
+
+const model_id = 'onnx-community/mgp-str-base';
+const model = await MgpstrForSceneTextRecognition.from_pretrained(model_id,);
+const processor = await MgpstrProcessor.from_pretrained(model_id);
+
+// Load image from the IIIT-5k dataset
+const url = "https://i.postimg.cc/ZKwLg2Gw/367-14.png";
+const image = await RawImage.read(url);
+
+// Preprocess the image
+const result = await processor(image);
+
+// Perform inference
+const outputs = await model(result);
+
+// Decode the model outputs
+const generated_text = processor.batch_decode(outputs.logits).generated_text;
+console.log(generated_text); // [ 'ticket' ]
+```
+
+---
+
 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`).