In this article, we are going to learn Text recognition from the camera in Flutter.
Text Recognition
Recognizes the text from the image/ real-time camera. The ML Kit Text Recognition API can recognize text in any Latin-based character set. Please check Google ML Kit for more on Text recognition.
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Using ML Kit for text recognition in Flutter
For text recognition in Flutter, we are going to use these flutter plugins: google_ml_kit and camera.
google_ml_kit: for the text recognition and many more, please check the official pub here.
camera: for accessing the device cameras, image stream which we are going to use later and for more check here.
Before we get started read about the platform-specific requirements here and known issues of the google_ml_kit plugin here.
And for the camera plugin platform-specific requirements check here and for the known issues check here.
We start by adding these plugins to our project’s pubspec.yaml file:
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dependencies: flutter: sdk: flutter google_ml_kit: ^0.7.3 camera: ^0.9.4+16 |
After adding the plugins, import them into your camera_screen.dart file like:
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import 'package:google_ml_kit/google_ml_kit.dart'; import 'package:camera/camera.dart'; |
Now define your camera controller and initialize it inside initState method and Initialize TextDetector like this:
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// Create the CameraController CameraController? _camera; // Initializing the TextDetector final textDetector = GoogleMlKit.vision.textDetector(); final String recognizedText = ""; @override void initState() { super.initState(); _initializeCamera(); // for camera initialization } void _initializeCamera() async { // Get list of cameras of the device List<CameraDescription> cameras = await availableCameras(); _camera = CameraController(cameras[0], ResolutionPreset.low); // Initialize the CameraController _camera?.initialize().then((_) async { // Start streaming images from platform camera await _camera ?.startImageStream((CameraImage image) => _processCameraImage(image)); // image processing and text recognition. }); } |
Once the camera is initialized, we start streaming images from the platform camera using the startImageStream method and pass the _processCameraImage method which is defined below as the parameter to it:
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void _processCameraImage(CameraImage image) async { // getting InputImage from CameraImage InputImage inputImage = getInputImage(image); final RecognisedText recognisedText = await textDetector.processImage(inputImage); // Using the recognised text. for (TextBlock block in recognisedText.blocks) { recognizedText = block.text + " "; } } |
As you can see in the above code, textDetector.processImage(inputImage) is the main method for recognizing the text from the image and requires an InputImage type image as a parameter so first, we need to get the InputImage for the recognition from the CameraImage and we are getting this using getInputImage(image) method defined below:
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InputImage getInputImage(CameraImage cameraImage) { final WriteBuffer allBytes = WriteBuffer(); for (Plane plane in cameraImage.planes) { allBytes.putUint8List(plane.bytes); } final bytes = allBytes.done().buffer.asUint8List(); final Size imageSize = Size(cameraImage.width.toDouble(), cameraImage.height.toDouble()); final InputImageRotation imageRotation = InputImageRotationMethods.fromRawValue( _camera!.description.sensorOrientation) ?? InputImageRotation.Rotation_0deg; final InputImageFormat inputImageFormat = InputImageFormatMethods.fromRawValue(cameraImage.format.raw) ?? InputImageFormat.NV21; final planeData = cameraImage.planes.map( (Plane plane) { return InputImagePlaneMetadata( bytesPerRow: plane.bytesPerRow, height: plane.height, width: plane.width, ); }, ).toList(); final inputImageData = InputImageData( size: imageSize, imageRotation: imageRotation, inputImageFormat: inputImageFormat, planeData: planeData, ); return InputImage.fromBytes(bytes: bytes, inputImageData: inputImageData); } |
Now create your CameraPreview widget for creating a preview widget for the given camera controller. and pass the above-defined CameraController like:
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// Preview widget for the given camera controller. CameraPreview( _camera!, child: Text("Show your recognized text${recognizedText}"), ) |
And put the above camera preview widget inside your build method like any other widget and that’s it.
Thanks for reading.
Happy Coding 🙂
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2 comments
Yes, it supports live text recognition. You can try it by making a sample app from the above code.