npm package: @milvoj/vision-camera-ocr
Description: VisionCamera Frame Processor Plugin to provide OCR support
Author: Milan Vojinovic
Latest version: 3.0.21-1


# @milvoj/vision-camera-ocr

A [VisionCamera](https://github.com/mrousavy/react-native-vision-camera) Frame Processor Plugin to preform text detection on images using [**MLKit Vision** Text Recognition](https://developers.google.com/ml-kit/vision/text-recognition). This module can be used only with React Native Vision Camera >= v4.x.x

<img style='width:200px;' src="docs/demo.gif">

## Installation

```sh
npm install @milvoj/vision-camera-ocr
cd ios && pod install
```

Add the plugin to your `babel.config.js`:

```js
module.exports = {
   plugins: [['react-native-worklets-core/plugin']],
    // ...
```

> Note: You have to restart metro-bundler for changes in the `babel.config.js` file to take effect.

## Usage

```js
import {OCRFrame, scanOCR} from 'vision-camera-ocr-plugin';
import {
  useFrameProcessor,
  Camera,
  useCameraDevice,
} from 'react-native-vision-camera';
import {Worklets} from 'react-native-worklets-core';

export default ({onTextClicked}: VisionCameraPlateProps) => {
  const [hasPermission, setHasPermission] = React.useState(false);
  const [ocr, setOcr] = React.useState<OCRFrame>();
  const isFocused = useIsFocused();
  const device = useCameraDevice('back');

  const onCodeDetected = Worklets.createRunInJsFn((data: any) => {
    setOcr(data);
  });

  const frameProcessor = useFrameProcessor(frame => {
    'worklet';
    const data = scanOCR(frame);
    onCodeDetected(data);
  }, []);

  React.useEffect(() => {
    (async () => {
      const status = await Camera.requestCameraPermission();
      setHasPermission(status === 'granted');
    })();
  }, []);


  return (
    <>
      {device !== undefined && hasPermission ? (
        <Camera
          frameProcessor={frameProcessor}
          device={device}
          isActive={isFocused}
          pixelFormat="yuv"
        />
      ) : (
        <View>
          <Text>No available cameras</Text>
        </View>
      )}
    </>
  );
};

```

## Data

`scanOCR(frame)` returns an `OCRFrame` with the following data shape. See the example for how to use this in your app.

 ``` jsx
  OCRFrame = {
    result: {
      text: string, // Raw result text
      blocks: Block[], // Each recognized element broken into blocks
    ;
};
```

The text object closely resembles the object documented in the MLKit documents.
<https://developers.google.com/ml-kit/vision/text-recognition#text_structure>

```
The Text Recognizer segments text into blocks, lines, and elements. Roughly speaking:

a Block is a contiguous set of text lines, such as a paragraph or column,

a Line is a contiguous set of words on the same axis, and

an Element is a contiguous set of alphanumeric characters ("word") on the same axis in most Latin languages, or a character in others
```

## Contributing

See the [contributing guide](CONTRIBUTING.md) to learn how to contribute to the repository and the development workflow.

## Credits

Fork from 
https://github.com/francesco-clementi-92/vision-camera-ocr
https://github.com/ismaelsousa/vision-camera-ocr
https://github.com/aarongrider/vision-camera-ocr

## License

MIT