Google's ML Kit Digital Ink Recognition for Flutter

A Flutter plugin to use Google's ML Kit Digital Ink Recognition to recognize handwritten text on a digital surface in hundreds of languages, as well as classify sketches.

• Google's ML Kit was build only for mobile platforms: iOS and Android apps.

• This plugin is not sponsored or maintained by Google. The authors are developers excited about Machine Learning that wanted to expose Google's native APIs to Flutter.

• Google's ML Kit APIs are only developed natively for iOS and Android. This plugin uses Flutter Platform Channels as explained here.

  Messages and responses are passed asynchronously, to ensure the user interface remains responsive. To read more about platform channels go here.

  Because this plugin uses platform channels, no Machine Learning processing is done in Flutter/Dart, all the calls are passed to the native platform using MethodChannel in Android and FlutterMethodChannel in iOS, and executed using Google's native APIs. Think of this plugin as a bridge between your app and Google's native ML Kit APIs. This plugin only passes the call to the native API and the processing is done by Google's API. It is important that you understand this concept when it comes to debugging errors for your ML model and/or app.

Requirements

iOS

• Minimum iOS Deployment Target: 12.0
• Xcode 13.2.1 or newer
• Swift 5
• ML Kit does not support 32-bit architectures (i386 and armv7). ML Kit does support 64-bit architectures (x86_64 and arm64). Check this list to see if your device has the required device capabilities. More info here.

Since ML Kit does not support 32-bit architectures (i386 and armv7), you need to exclude armv7 architectures in Xcode in order to run flutter build ios or flutter build ipa. More info here.

Go to Project > Runner > Building Settings > Excluded Architectures > Any SDK > armv7

Your Podfile should look like this:

platform :ios, '12.0'  # or newer version

...

# add this line:
$iOSVersion = '12.0'  # or newer version

post_install do |installer|
  # add these lines:
  installer.pods_project.build_configurations.each do |config|
    config.build_settings["EXCLUDED_ARCHS[sdk=*]"] = "armv7"
    config.build_settings['IPHONEOS_DEPLOYMENT_TARGET'] = $iOSVersion
  end
  
  installer.pods_project.targets.each do |target|
    flutter_additional_ios_build_settings(target)
    
    # add these lines:
    target.build_configurations.each do |config|
      if Gem::Version.new($iOSVersion) > Gem::Version.new(config.build_settings['IPHONEOS_DEPLOYMENT_TARGET'])
        config.build_settings['IPHONEOS_DEPLOYMENT_TARGET'] = $iOSVersion
      end
    end
    
  end
end

Notice that the minimum IPHONEOS_DEPLOYMENT_TARGET is 12.0, you can set it to something newer but not older.

Android

• minSdkVersion: 21
• targetSdkVersion: 33
• compileSdkVersion: 33

Usage

Import

import 'package:digital_ink_recognition_mlkit/digital_ink_recognition_mlkit.dart';

Digital Ink Recognition

Create an instance of DigitalInkRecognizer

String languageCode; // BCP-47 Code from https://developers.google.com/ml-kit/vision/digital-ink-recognition/base-models?hl=en#text
final digitalInkRecognizer = DigitalInkRecognizer(languageCode: languageCode);

Managing remote models

Download model

final bool response = await digitalInkRecognizer.downLoadModel(model);

Returns true if model downloads successfully or model is already downloaded. On failing to download it throws an error.

Delete model

final bool response = await digitalInkRecognizer.deleteModel(model);

Returns true if model is deleted successfully or model is not present.

Process ink

final p1 = StrokePoint(x: x1, y: y1, t: DateTime.now().millisecondsSinceEpoch); // make sure that `t` is a long
final p2 = StrokePoint(x: x1, y: y1, t: DateTime.now().millisecondsSinceEpoch); // make sure that `t` is a long

Stroke stroke1 = Stroke(); // it contains all of the StrokePoint
stroke1.points = [p1, p2, ...]

Ink ink = Ink(); // it contains all of the Stroke
ink.strokes = [stroke1, stroke2, ...];

final List<RecognitionCandidate> candidates = await digitalInkRecognizer.recognize(ink);

for (final candidate in candidates) {
  final text = candidate.text;
  final score = candidate.score;
}

Make sure you download the language model before processing any Ink.

To improve the accuracy of text recognition you can set an writing area and pre-context. More details here.

String preContext;
double width;
double height;
final context = DigitalInkRecognitionContext(
  preContext: preContext,
  writingArea: WritingArea(width: width, height: height),
);

final List<RecognitionCandidate> candidates = await digitalInkRecognizer.recognize(ink, context: context);

Release resources with close()

digitalInkRecognizer.close();

Libraries

digital_ink_recognition_mlkit