Google ML Kit for Firebase

pub package

A Flutter plugin to use the Google ML Kit for Firebase API.

For Flutter plugins for other Firebase products, see FlutterFire.md.

Note: This plugin is still under development, and some APIs might not be available yet. Feedback and Pull Requests are most welcome!

Usage

To use this plugin, add firebase_ml_vision as a dependency in your pubspec.yaml file. You must also configure Firebase for each platform project: Android and iOS (see the example folder or https://codelabs.developers.google.com/codelabs/flutter-firebase/#4 for step by step details).

Android

Optional but recommended: If you use the on-device API, configure your app to automatically download the ML model to the device after your app is installed from the Play Store. To do so, add the following declaration to your app's AndroidManifest.xml file:

<application ...>
  ...
  <meta-data
      android:name="com.google.firebase.ml.vision.DEPENDENCIES"
      android:value="ocr" />
  <!-- To use multiple models: android:value="ocr,model2,model3" -->
</application>

On-device Text Recognition

To use the on-device text recognition model, run the text detector as described below:

  1. Create a FirebaseVisionImage object from your image.

To create a FirebaseVisionImage from an image File object:

final File imageFile = getImageFile();
final FirebaseVisionImage visionImage = FirebaseVisionImage.fromFile(imageFile);
  1. Get an instance of TextDetector and pass visionImage to detectInImage().
final TextDetector detector = FirebaseVision.instance.getTextDetector();
final List<TextBlock> blocks = await detector.detectInImage(visionImage);

detector.close();
  1. Extract text and text locations from blocks of recognized text.
for (TextBlock block in textLocations) {
  final Rectangle<num> boundingBox = block.boundingBox;
  final List<Point<num>> cornerPoints = block.cornerPoints;
  final String text = block.text;

  for (TextLine line in block.lines) {
    // ...

    for (TextElement element in line.elements) {
      // ...
    }
  }
}

Getting Started

See the example directory for a complete sample app using Google ML Kit for Firebase.

Libraries

firebase_ml_vision

Dart

dart:ui
Built-in types and core primitives for a Flutter application. [...]

Core

dart:async
Support for asynchronous programming, with classes such as Future and Stream. [...]
dart:collection
Classes and utilities that supplement the collection support in dart:core. [...]
dart:convert
Encoders and decoders for converting between different data representations, including JSON and UTF-8. [...]
dart:core
Built-in types, collections, and other core functionality for every Dart program. [...]
dart:developer
Interact with developer tools such as the debugger and inspector. [...]
dart:math
Mathematical constants and functions, plus a random number generator. [...]
dart:typed_data
Lists that efficiently handle fixed sized data (for example, unsigned 8 byte integers) and SIMD numeric types. [...]

VM

dart:io
File, socket, HTTP, and other I/O support for non-web applications. [...]
dart:isolate
Concurrent programming using isolates: independent workers that are similar to threads but don't share memory, communicating only via messages. [...]