M.2 Recognize Text

Text Recognition

With ML Kit's text recognition APIs, you can recognize text in any Latin-based language (and more, with Cloud-based text recognition).

Text recognition can automate tedious data entry for credit cards, receipts, and business cards, or help organize photos. With the Cloud-based API, you can extract text from documents, which you can use to increase accessibility or translate documents. Apps can even keep track of real-world objects, such as by reading the numbers on trains.

NOTICE: This is a beta release of ML Kit for Firebase. This API might be changed in backward-incompatible ways and is not subject to any SLA or deprecation policy.

You can use ML Kit to recognize text in images. ML Kit has both a general-purpose API suitable for recognizing text in images, such as the text of a street sign, and an API optimized for recognizing the text of documents. The general-purpose API has both on-device and cloud-based models. Document text recognition is available only as a cloud-based model. See the overview for a comparison of the cloud and on-device models.

Create a VisionImage first:

import com.myflashlab.air.extensions.firebase.mlkit.*;

// initialize ML Kit AFTER FirebaseCore is successfully started
Mlkit.init();

/*
    You can create VisionImage instance from BitmapData, File or ByteArray

    Mlkit.createVisionImageFromBitmapData
    Mlkit.createVisionImageFromFile
    Mlkit.createVisionImageFromBytes
*/

var image:File = File.applicationDirectory.resolvePath("img.jpg");

// This is beta version and for some reason, creating vision 
// images from file is not working! instead you may use 
// BitmapData for now!

var loader:Loader = new Loader();
loader.load(new URLRequest(image.url));
loader.contentLoaderInfo.addEventListener(Event.COMPLETE, function loaderComplete(e:Event):void
{
	var bm:Bitmap = e.target.content as Bitmap;

	// create a VisionImage
	var visionImg:VisionImage = Mlkit.createVisionImageFromBitmapData(bm.bitmapData);
				
	trace("visionImg.id = " + visionImg.id);
});

Extract information from the image

// (Optional) initialize an instance of TextRecognizerCloudOptions
//var options:TextRecognizerCloudOptions = new TextRecognizerCloudOptions();
//options.languageHints = 
//options.modelType = 
//...

// get an instance of TextRecognizer
var detector:TextRecognizer = Mlkit.initTextRecognizer(Mlkit.ON_DEVICE, options);

// process the image and wait for results
detector.process(visionImg, function ($visionText:VisionText, $error:Error):void
{
	if($error) trace($error.message);
				
	if($visionText) trace($visionText.toString());
				
	detector.close();
});