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