Implementing ML Kit Text Recognition v2 on Android

In this article, we'll explore how to implement ML Kit's Text Recognition v2 in your Android application. ML Kit provides a powerful set of tools for on-device machine learning, and text recognition is one of its standout features. By the end of this guide, you will have a functional text recognition feature in your Android app. Prerequisites Before we begin, ensure you have the following: - Android Studio installed on your machine. - A basic understanding of Android development and familiarity with Kotlin or Java. - An Android device or emulator for testing. Setting Up Your Android Project First, create a new Android project in Android Studio. Here’s how: 1. Open Android Studio and select "New Project". 2. Choose "Empty Activity" and click "Next". 3. Name your project (e.g., TextRecognitionApp) and set the language to Kotlin or Java. 4. Click "Finish" to create the project. Adding ML Kit Dependencies To use ML Kit for text recognition, you need to add the required dependencies in your build.gradle file. Open the app/build.gradle file and add the following lines in the dependencies block: groovy implementation 'com.google.mlkit:text-recognition:2.2.0' Make sure to sync your project after adding the dependencies. Configuring Camera Permissions Since you'll be capturing images for text recognition, you need to request camera permissions. Add the following permissions to your AndroidManifest.xml file: xml <uses-permission android:name="android.permission.CAMERA"/ <uses-permission android:name="android.permission.INTERNET"/ You will also need to handle runtime permissions for devices running Android 6.0 (API level 23) or higher. Implementing the Camera Next, we need to implement a camera feature to capture images. You can use the CameraX library, which simplifies camera operations. Start by adding the CameraX dependencies to your build.gradle file: groovy implementation 'androidx.camera:camera-core:1.0.0' implementation 'androidx.camera:camera-camera2:1.0.0' implementation 'androidx.camera:camera-lifecycle:1.0.0' implementation 'androidx.camera:camera-view:1.0.0' Now, in your main activity, set up the camera. Here’s an example of how to do this in Kotlin: kotlin private fun startCamera() { val cameraProviderFuture = ProcessCameraProvider.getInstance(this) cameraProviderFuture.addListener(Runnable { val cameraProvider: ProcessCameraProvider = cameraProviderFuture.get() val preview = Preview.Builder().build().also { it.setSurfaceProvider(viewFinder.surfaceProvider) } val cameraSelector = CameraSelector.DEFAULTBACKCAMERA try { cameraProvider.unbindAll() cameraProvider.bindToLifecycle(this, cameraSelector, preview) } catch (exc: Exception) { Log.e(TAG, "Use case binding failed", exc) } }, ContextCompat.getMainExecutor(this)) } Ensure you have a PreviewView in your layout XML to display the camera feed. Capturing Images for Text Recognition To perform text recognition, you'll need to capture images from the camera. You can do this by adding an ImageAnalysis use case to your camera setup. Here’s an example: kotlin val imageAnalysis = ImageAnalysis.Builder() .setTargetResolution(Size(1280, 720)) .setBackpressureStrategy(ImageAnalysis.STRATEGYKEEPONLYLATEST) .build() imageAnalysis.setAnalyzer(ContextCompat.getMainExecutor(this), ImageAnalyzer()) cameraProvider.bindToLifecycle(this, cameraSelector, preview, imageAnalysis) Next, implement the ImageAnalyzer class that will process the captured images: kotlin private inner class ImageAnalyzer : ImageAnalysis.Analyzer { override fun analyze(image: ImageProxy) { // Process the image for text recognition here image.close() } } Performing Text Recognition Now, let’s integrate ML Kit’s text recognition within the analyze method of your ImageAnalyzer. Here's how to do it: kotlin private inner class ImageAnalyzer : ImageAnalysis.Analyzer { override fun analyze(image: ImageProxy) { val mediaImage = image.image if (mediaImage != null) { val inputImage = InputImage.fromMediaImage(mediaImage, image.imageInfo.rotationDegrees) textRecognizer.process(inputImage) .addOnSuccessListener { visionText - // Handle the recognized text Log.d(TAG, "Recognized text: ${visionText.text}") } .addOnFailureListener { e - Log.e(TAG, "Text recognition failed", e) } .addOnCompleteListener { image.close() } } } } In this code, we use InputImage.fromMediaImage to convert the captured image into a format that ML Kit can process. The process method then performs text recognition, returning the recognized text or an error if it fails. Testing Your Application Once you have integrated the above code, you can run your application on an Android device or emulator. Verify that the camera opens properly, and point it at some text. The recognized text should appear in the logs. Conclusion You've successfully integrated ML Kit's Text Recognition v2 into your Android application. With this powerful feature, you can enhance your app's functionality by enabling it to read and process text in real-time. For further details and a visual walkthrough, be sure to check out the video tutorial: Watch the full tutorial on YouTube(https://www.youtube.com/watch?v=m13bCZX8vjY). By following this guide, you can now explore more advanced applications of text recognition, such as translating text or searching for information based on recognized words. Happy coding!