Recognize text in images with ML Kit on Android

You can use ML Kit to recognize text in images, such as the text of a street sign.

See the ML Kit quickstart sample on GitHub for an example of this API in use, or try the codelab.

The text recognition API uses an unbundled model that must be downloaded. You have the option of performing the download when the app is installed or when it is first launched.

Before you begin

  1. In your project-level build.gradle file, make sure to include Google's Maven repository in both your buildscript and allprojects sections.
  2. Add the dependencies for the ML Kit Android libraries to your module's app-level gradle file, which is usually app/build.gradle:
    dependencies {
      // ...
    
      implementation 'com.google.android.gms:play-services-mlkit-text-recognition:16.1.1'
    }
    
  3. Optional but recommended: You can configure your app to automatically download the ML model to the device after your app is installed from the Play Store. To do so, add the following declaration to your app's AndroidManifest.xml file:

    <application ...>
      ...
      <meta-data
          android:name="com.google.mlkit.vision.DEPENDENCIES"
          android:value="ocr" />
      <!-- To use multiple models: android:value="ocr,model2,model3" -->
    </application>
    
    If you do not enable install-time model downloads, the model will be downloaded the first time you run the on-device detector. Requests you make before the download has completed will produce no results.

Now you are ready to start recognizing text in images.

Input image guidelines

  • For ML Kit to accurately recognize text, input images must contain text that is represented by sufficient pixel data. Ideally, each character should be at least 16x16 pixels. There is generally no accuracy benefit for characters to be larger than 24x24 pixels.

    So, for example, a 640x480 image might work well to scan a business card that occupies the full width of the image. To scan a document printed on letter-sized paper, a 720x1280 pixel image might be required.

  • Poor image focus can affect text recognition accuracy. If you aren't getting acceptable results, try asking the user to recapture the image.

  • If you are recognizing text in a real-time application, you should consider the overall dimensions of the input images. Smaller images can be processed faster. To reduce latency, ensure that the text occupies as much of the image as possible, and capture images at lower resolutions (keeping in mind the accuracy requirements mentioned above). For more information, see Tips to improve real-time performance.


Recognize text in images

To recognize text in an image, run the text recognizer as described below.

1. Prepare the input image

To recognize text in an image, create an InputImage object from either a Bitmap, media.Image, ByteBuffer, byte array, or a file on the device. Then, pass the InputImage object to the TextRecognizer's processImage method.

You can create an InputImage from different sources, each is explained below.

Using a media.Image

To create an InputImage object from a media.Image object, such as when you capture an image from a device's camera, pass the media.Image object and the image's rotation to InputImage.fromMediaImage().

If you use the CameraX library, the OnImageCapturedListener and ImageAnalysis.Analyzer classes calculate the rotation value for you.

Kotlin

private class YourImageAnalyzer : ImageAnalysis.Analyzer {

    override fun analyze(imageProxy: ImageProxy) {
        val mediaImage = imageProxy.image
        if (mediaImage != null) {
            val image = InputImage.fromMediaImage(mediaImage, imageProxy.imageInfo.rotationDegrees)
            // Pass image to an ML Kit Vision API
            // ...
        }
    }
}

Java

private class YourAnalyzer implements ImageAnalysis.Analyzer {

    @Override
    public void analyze(ImageProxy imageProxy) {
        Image mediaImage = imageProxy.getImage();
        if (mediaImage != null) {
          InputImage image =
                InputImage.fromMediaImage(mediaImage, imageProxy.getImageInfo().getRotationDegrees());
          // Pass image to an ML Kit Vision API
          // ...
        }
    }
}

If you don't use a camera library that gives you the image's rotation degree, you can calculate it from the device's rotation degree and the orientation of camera sensor in the device:

Kotlin

private val ORIENTATIONS = SparseIntArray()

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

/**
 * Get the angle by which an image must be rotated given the device's current
 * orientation.
 */
@RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)
@Throws(CameraAccessException::class)
private fun getRotationCompensation(cameraId: String, activity: Activity, isFrontFacing: Boolean): Int {
    // Get the device's current rotation relative to its "native" orientation.
    // Then, from the ORIENTATIONS table, look up the angle the image must be
    // rotated to compensate for the device's rotation.
    val deviceRotation = activity.windowManager.defaultDisplay.rotation
    var rotationCompensation = ORIENTATIONS.get(deviceRotation)

    // Get the device's sensor orientation.
    val cameraManager = activity.getSystemService(CAMERA_SERVICE) as CameraManager
    val sensorOrientation = cameraManager
            .getCameraCharacteristics(cameraId)
            .get(CameraCharacteristics.SENSOR_ORIENTATION)!!

    if (isFrontFacing) {
        rotationCompensation = (sensorOrientation + rotationCompensation) % 360
    } else { // back-facing
        rotationCompensation = (sensorOrientation - rotationCompensation + 360) % 360
    }
    return rotationCompensation
}

Java

private static final SparseIntArray ORIENTATIONS = new SparseIntArray();
static {
    ORIENTATIONS.append(Surface.ROTATION_0, 0);
    ORIENTATIONS.append(Surface.ROTATION_90, 90);
    ORIENTATIONS.append(Surface.ROTATION_180, 180);
    ORIENTATIONS.append(Surface.ROTATION_270, 270);
}

/**
 * Get the angle by which an image must be rotated given the device's current
 * orientation.
 */
@RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)
private int getRotationCompensation(String cameraId, Activity activity, boolean isFrontFacing)
        throws CameraAccessException {
    // Get the device's current rotation relative to its "native" orientation.
    // Then, from the ORIENTATIONS table, look up the angle the image must be
    // rotated to compensate for the device's rotation.
    int deviceRotation = activity.getWindowManager().getDefaultDisplay().getRotation();
    int rotationCompensation = ORIENTATIONS.get(deviceRotation);

    // Get the device's sensor orientation.
    CameraManager cameraManager = (CameraManager) activity.getSystemService(CAMERA_SERVICE);
    int sensorOrientation = cameraManager
            .getCameraCharacteristics(cameraId)
            .get(CameraCharacteristics.SENSOR_ORIENTATION);

    if (isFrontFacing) {
        rotationCompensation = (sensorOrientation + rotationCompensation) % 360;
    } else { // back-facing
        rotationCompensation = (sensorOrientation - rotationCompensation + 360) % 360;
    }
    return rotationCompensation;
}

Then, pass the media.Image object and the rotation degree value to InputImage.fromMediaImage():

Kotlin

val image = InputImage.fromMediaImage(mediaImage, rotation)

Java

InputImage image = InputImage.fromMediaImage(mediaImage, rotation);

Using a file URI

To create an InputImage object from a file URI, pass the app context and file URI to InputImage.fromFilePath(). This is useful when you use an ACTION_GET_CONTENT intent to prompt the user to select an image from their gallery app.

Kotlin

val image: InputImage
try {
    image = InputImage.fromFilePath(context, uri)
} catch (e: IOException) {
    e.printStackTrace()
}

Java

InputImage image;
try {
    image = InputImage.fromFilePath(context, uri);
} catch (IOException e) {
    e.printStackTrace();
}

Using a ByteBuffer or ByteArray

To create an InputImage object from a ByteBuffer or a ByteArray, first calculate the image rotation degree as previously described for media.Image input. Then, create the InputImage object with the buffer or array, together with image's height, width, color encoding format, and rotation degree:

Kotlin

val image = InputImage.fromByteBuffer(
        byteBuffer,
        /* image width */ 480,
        /* image height */ 360,
        rotationDegrees,
        InputImage.IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12
)
// Or:
val image = InputImage.fromByteArray(
        byteArray,
        /* image width */ 480,
        /* image height */ 360,
        rotationDegrees,
        InputImage.IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12
)

Java

InputImage image = InputImage.fromByteBuffer(byteBuffer,
        /* image width */ 480,
        /* image height */ 360,
        rotationDegrees,
        InputImage.IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12
);
// Or:
InputImage image = InputImage.fromByteArray(
        byteArray,
        /* image width */480,
        /* image height */360,
        rotation,
        InputImage.IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12
);

Using a Bitmap

To create an InputImage object from a Bitmap object, make the following declaration:

Kotlin

val image = InputImage.fromBitmap(bitmap, 0)

Java

InputImage image = InputImage.fromBitmap(bitmap, rotationDegree);

The image is represented by a Bitmap object together with rotation degrees.

2. Get an instance of TextRecognizer

Kotlin

val recognizer = TextRecognition.getClient()

Java

TextRecognizer recognizer = TextRecognition.getClient();

3. Process the image

Pass the image to the process method:

Kotlin

val result = recognizer.process(image)
        .addOnSuccessListener { visionText ->
            // Task completed successfully
            // ...
        }
        .addOnFailureListener { e ->
            // Task failed with an exception
            // ...
        }

Java

Task<Text> result =
        recognizer.process(image)
                .addOnSuccessListener(new OnSuccessListener<Text>() {
                    @Override
                    public void onSuccess(Text visionText) {
                        // Task completed successfully
                        // ...
                    }
                })
                .addOnFailureListener(
                        new OnFailureListener() {
                            @Override
                            public void onFailure(@NonNull Exception e) {
                                // Task failed with an exception
                                // ...
                            }
                        });

4. Extract text from blocks of recognized text

If the text recognition operation succeeds, a Text object is passed to the success listener. A Text object contains the full text recognized in the image and zero or more TextBlock objects.

Each TextBlock represents a rectangular block of text, which contains zero or more Line objects. Each Line object contains zero or more Element objects, which represent words and word-like entities such as dates and numbers.

For each TextBlock, Line, and Element object, you can get the text recognized in the region and the bounding coordinates of the region.

For example:

Kotlin

val resultText = result.text
for (block in result.textBlocks) {
    val blockText = block.text
    val blockCornerPoints = block.cornerPoints
    val blockFrame = block.boundingBox
    for (line in block.lines) {
        val lineText = line.text
        val lineCornerPoints = line.cornerPoints
        val lineFrame = line.boundingBox
        for (element in line.elements) {
            val elementText = element.text
            val elementCornerPoints = element.cornerPoints
            val elementFrame = element.boundingBox
        }
    }
}

Java

String resultText = result.getText();
for (Text.TextBlock block : result.getTextBlocks()) {
    String blockText = block.getText();
    Point[] blockCornerPoints = block.getCornerPoints();
    Rect blockFrame = block.getBoundingBox();
    for (Text.Line line : block.getLines()) {
        String lineText = line.getText();
        Point[] lineCornerPoints = line.getCornerPoints();
        Rect lineFrame = line.getBoundingBox();
        for (Text.Element element : line.getElements()) {
            String elementText = element.getText();
            Point[] elementCornerPoints = element.getCornerPoints();
            Rect elementFrame = element.getBoundingBox();
        }
    }
}

Tips to improve real-time performance

To recognize text in a real-time application, follow these guidelines to achieve the best framerates:

  • If you use the Camera or camera2 API, throttle calls to the text recognizer. If a new video frame becomes available while the text recognizer is running, drop the frame. See the VisionProcessorBase class in the quickstart sample app for an example.
  • If you use the CameraX API, be sure that backpressure strategy is set to its default value ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST. This guarantees only one image will be delivered for analysis at a time. If more images are produced when the analyzer is busy, they will be dropped automatically and not queued for delivery. Once the image being analyzed is closed by calling ImageProxy.close(), the next latest image will be delivered.
  • If you use the output of the text recognizer to overlay graphics on the input image, first get the result from ML Kit, then render the image and overlay in a single step. This renders to the display surface only once for each input frame. See the CameraSourcePreview and GraphicOverlay classes in the quickstart sample app for an example.
  • If you use the Camera2 API, capture images in ImageFormat.YUV_420_888 format. If you use the older Camera API, capture images in ImageFormat.NV21 format.
  • Consider capturing images at a lower resolution. However, also keep in mind this API's image dimension requirements.

Next steps