By combining the Jetpack camerax and firebase ML kit you can search text inside an image. In this article we want to explore examples.

(a). CameraX Search Text inside an image

An example that uses the Jetpack CameraX api. The app takes input for a phrase from the user, and then uses CameraX and MLKit Text Recognition to preview the camera feed, analyze the image buffer to search for the phrase, and capture the image once the phrase has been detected.

Here is the demo:

Search Text inside an Image

Step 1: Setup Firebase

because this example uses Firebase technologies you need to add the google-services.json to the project. So you must create a Firebase project and add the google-services.json, first.

Step 2: Add dependencies

Once you’ve added the google-services.json in the project you proceed to setup dependencies. You need to add dependencies for CameraX in your app-level build.gradle;

    implementation "${camerax_version}"
    implementation "${camerax_version}"

Then add firebase ML and Firebase core:

    implementation ''
    implementation ''

Also Glide will be used to load image:

    implementation 'com.github.bumptech.glide:glide:4.9.0'
    annotationProcessor 'com.github.bumptech.glide:compiler:4.9.0'

Step 3: Create Layouts

Next you create layouts. There will be four layouts:

  1. activity_main.xm;
  2. fragment_camera.xml
  3. fragment_photo.xml
  4. fragment_phrase_entry.xml


This layout will have the TextureView:

<?xml version="1.0" encoding="utf-8"?>
<androidx.constraintlayout.widget.ConstraintLayout xmlns:android=""

        app:layout_constraintTop_toTopOf="parent" />


You can find the other xml files in the source code reference.

Step 4: Write Code

There are 4 kotlin files;

  1. CameraFragment.kt
  2. PhotoFragment.kt
  3. PhraseEntryFragment.kt
  4. AutoFitPreviewBuilder.kt
  5. MainActivity.kt


Start by extending the Fragment class:

class CameraFragment : Fragment() {

Create an inner TextAnalyzer class that takes two parameters as follows:

class TextAnalyzer(
    private val identifier: String,
    private val identifierDetectedCallback: () -> Unit
) : ImageAnalysis.Analyzer {

In a companion object prepare a sparseArray with orientations for FirebaseVisionImageMetadata:

    companion object {
        private val ORIENTATIONS = SparseIntArray()

        init {
            ORIENTATIONS.append(0, FirebaseVisionImageMetadata.ROTATION_0)
            ORIENTATIONS.append(90, FirebaseVisionImageMetadata.ROTATION_90)
            ORIENTATIONS.append(180, FirebaseVisionImageMetadata.ROTATION_180)
            ORIENTATIONS.append(270, FirebaseVisionImageMetadata.ROTATION_270)

Create a function to obtain orientations from rotation:

    private fun getOrientationFromRotation(rotationDegrees: Int): Int {
        return when (rotationDegrees) {
            0 -> FirebaseVisionImageMetadata.ROTATION_0
            90 -> FirebaseVisionImageMetadata.ROTATION_90
            180 -> FirebaseVisionImageMetadata.ROTATION_180
            270 -> FirebaseVisionImageMetadata.ROTATION_270
            else -> FirebaseVisionImageMetadata.ROTATION_90

Override the analyze() function with the following code:

 override fun analyze(image: ImageProxy?, rotationDegrees: Int) {
        if (image?.image == null || image.image == null) return

        val timestamp = System.currentTimeMillis()
        // only run once per second
        if (timestamp - lastAnalyzedTimestamp >= TimeUnit.SECONDS.toMillis(1)) {
            val visionImage = FirebaseVisionImage.fromMediaImage(

            val detector = FirebaseVision.getInstance()

                .addOnSuccessListener { result: FirebaseVisionText ->
                    // remove the new lines and join to a single string,
                    // then search for our identifier
                    val textToSearch = result.text.split("n").joinToString(" ")
                    if (textToSearch.contains(identifier, true)) {
                .addOnFailureListener {
                    Log.e(TAG, "Error processing image", it)
            lastAnalyzedTimestamp = timestamp

` As a function inside the fragment create startCamera() to launch the camera:

    private fun startCamera() {

In it start by unbinding anything that might still be open using the unbindAll() function:


Get the necessary metrics:

        val metrics = DisplayMetrics().also { surfacePreview.display.getRealMetrics(it) }
        val screenSize = Size(metrics.widthPixels, metrics.heightPixels)
        val screenAspectRatio = Rational(metrics.widthPixels, metrics.heightPixels)

Build the preview configurations using the above metrics;

        val previewConfig = PreviewConfig.Builder()

Build the viewfinder use case:

        val preview =, surfacePreview)

Setup the Analyzer configurations:

        val analyzerConfig = ImageAnalysisConfig.Builder().apply {
            val analyzerThread = HandlerThread("OCR").apply { start() }
            setTargetResolution(Size(1280, 720))

Also setup the capture configurations:

        val captureConfig = ImageCaptureConfig.Builder()

Instantiate the ImageCapture and ImageAnalyzer classes,passing in the capture and analyzer configurations respectively:

        imageCapture = ImageCapture(captureConfig)
        val imageAnalysis = ImageAnalysis(analyzerConfig)

Set the TextAnalyzer to analyzer property of the imageAnalysis:

        imageAnalysis.analyzer = TextAnalyzer(phrase) {
            val outputDirectory: File = requireContext().filesDir
            val photoFile = File(outputDirectory, "${System.currentTimeMillis()}.jpg")
            imageCapture?.takePicture(photoFile, imageCaptureListener, ImageCapture.Metadata())

Bind CameraX to the fragment lifeycle using the bindToLifecycle() function, passing in the context, preview, imageAnalysis and imageCapture objects:

        CameraX.bindToLifecycle(this, preview, imageAnalysis, imageCapture)

Find the classes in the source code.


Below are the source code reference links for this project:

No. Link
1. Download codehere
2. Download codehere
3. Follow Project Authorhere