---
title: Text Recognition
description: Get started with ML Kit Vision Text Recognition.
next: /ml-vision/landmark-recognition
previous: /ml-vision/usage
---

Text recognition can automate tedious data entry for credit cards, receipts, and business cards. With the Cloud-based API,
you can also extract text from pictures of documents, which you can use to increase accessibility or translate documents.

Once a file has been processed, the API returns a [`VisionDocumentText`](/reference/ml-vision/visiondocumenttext), referencing
all found text along with each [`VisionDocumentTextBlock`](/reference/ml-vision/visiondocumenttextblock). Each block contains
meta-data such as:

- The 4-point coordinates of the box on the document.
- Paragraphs within the block.
- Recognized languages within the block/document.
- The confidence the Machine Learning service has in it's own results.

# Cloud Text Recognition

The cloud based text recognition service uploads a given document to the remote Firebase service which processes the results and returns them.
To get started, call the `cloudDocumentTextRecognizerProcessImage` method with a path to a local file on your device:

```js
import { utils } from '@react-native-firebase/app';
import vision from '@react-native-firebase/ml-vision';

async function processDocument(localPath) {
  const processed = await vision().cloudDocumentTextRecognizerProcessImage(localPath);

  console.log('Found text in document: ', processed.text);

  processed.blocks.forEach(block => {
    console.log('Found block with text: ', block.text);
    console.log('Confidence in block: ', block.confidence);
    console.log('Languages found in block: ', block.recognizedLanguages);
  });
}

// Local path to file on the device
const localFile = `${utils.FilePath.PICTURES_DIRECTORY}/text-document.jpg`;

processDocument(localFile).then(() => console.log('Finished processing file.'));
```

To learn more about the available data on a processed document, view the [`VisionDocumentText`](/reference/ml-vision/visiondocumenttext)
documentation.

## Configuration

To help improve the results when using the cloud service, you can optionally provide arguments to the `cloudDocumentTextRecognizerProcessImage`
method:

```js
const processed = await vision().cloudDocumentTextRecognizerProcessImage(documentPath, {
  // The document contains Kurdish
  languageHints: ['KU'],
});
```

In most scenarios, not providing any hints will yield better results. Use this configuration if the cloud service is struggling
to detect a language.

View the [`VisionCloudDocumentTextRecognizerOptions`](/reference/ml-vision/visionclouddocumenttextrecognizeroptions) documentation for more information.

# On-device Text Recognition

Running the ML Kit service on a device requires the `ml_vision_ocr_model` to be download to the device. Although the results
of on-device processing will be faster and more accurate, including the model in your application will increase the size
of the application.

## Enable the model

To enable the mode, set the `ml_vision_ocr_model` key to `true` in your `firebase.json` file:

```json
// <project-root>/firebase.json
{
  "react-native": {
    "ml_vision_ocr_model": true
  }
}
```

Once complete, rebuild your application:

```bash
# For Android
npx react-native run-android

# For iOS
cd ios/ && pod install --repo-update
npx react-native run-ios
```

## Process

Once the model has been downloaded, call the `textRecognizerProcessImage` method with a path to a local file on your device:

```js
import { utils } from '@react-native-firebase/app';
import vision from '@react-native-firebase/ml-vision';

async function processDocument(localPath) {
  const processed = await vision().textRecognizerProcessImage(localPath);

  console.log('Found text in document: ', processed.text);

  processed.blocks.forEach(block => {
    console.log('Found block with text: ', block.text);
    console.log('Confidence in block: ', block.confidence);
    console.log('Languages found in block: ', block.recognizedLanguages);
  });
}

// Local path to file on the device
const localFile = `${utils.FilePath.PICTURES_DIRECTORY}/text-document.jpg`;

processDocument(localFile).then(() => console.log('Finished processing file.'));
```