Scan Barcodes with ML Kit on Android

You can use ML Kit to recognize and decode barcodes.

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

Before you begin

  1. If you have not already added Firebase to your app, do so by following the steps in the getting started guide.
  2. Include the dependencies for ML Kit in your app-level build.gradle file:
    dependencies {
      // ...
      implementation ''
  3. Optional but recommended: 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 ...>
          android:value="barcode" />
      <!-- To use multiple models: android:value="barcode,model2,model3" -->
    If you do not enable install-time model downloads, the model will be downloaded the first time you run the detector. Requests you make before the download has completed will produce no results.

Input image guidelines

  • For ML Kit to accurately read barcodes, input images must contain barcodes that are represented by sufficient pixel data. In general, the smallest meaningful unit of the barcode should be at least 2 pixels wide (and for 2-dimensional codes, 2 pixels tall).

    For example, EAN-13 barcodes are made up of bars and spaces that are 1, 2, 3, or 4 units wide, so an EAN-13 barcode image ideally has bars and spaces that are at least 2, 4, 6, and 8 pixels wide. Because an EAN-13 barcode is 95 units wide in total, the barcode should be at least 190 pixels wide.

    Denser formats, such as PDF417, need greater pixel dimensions for ML Kit to reliably read them. For example, a PDF417 code can have up to 34 17-unit wide "words" in a single row, which would ideally be at least 1156 pixels wide.

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

  • If you are scanning barcodes in a real-time application, you might also want to consider the overall dimensions of the input images. Smaller images can be processed faster, so to reduce latency, capture images at lower resolutions (keeping in mind the above accuracy requirements) and ensure that the barcode occupies as much of the image as possible. Also see Tips to improve real-time performance.

1. Configure the barcode detector

If you know which barcode formats you expect to read, you can improve the speed of the barcode detector by configuring it to only detect those formats.

For example, to detect only Aztec code and QR codes, build a FirebaseVisionBarcodeDetectorOptions object as in the following example:

FirebaseVisionBarcodeDetectorOptions options =
        new FirebaseVisionBarcodeDetectorOptions.Builder()

The following formats are supported:

  • Code 128 (FORMAT_CODE_128)
  • Code 39 (FORMAT_CODE_39)
  • Code 93 (FORMAT_CODE_93)
  • Codabar (FORMAT_CODABAR)
  • EAN-13 (FORMAT_EAN_13)
  • EAN-8 (FORMAT_EAN_8)
  • PDF417 (FORMAT_PDF417)
  • Aztec (FORMAT_AZTEC)
  • Data Matrix (FORMAT_DATA_MATRIX)

2. Run the barcode detector

To recognize barcodes in an image, create a FirebaseVisionImage object from either a Bitmap, media.Image, ByteBuffer, byte array, or a file on the device. Then, pass the FirebaseVisionImage object to the FirebaseVisionBarcodeDetector's detectInImage method.

  1. Create a FirebaseVisionImage object from your image.

    • To create a FirebaseVisionImage object from a Bitmap object:
      FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
      The image represented by the Bitmap object must be upright, with no additional rotation required.
    • To create a FirebaseVisionImage object from a media.Image object, such as when capturing an image from a device's camera, first determine the angle the image must be rotated to compensate for both the device's rotation and the orientation of camera sensor in the device:
      private static final SparseIntArray ORIENTATIONS = new SparseIntArray();
      static {
          ORIENTATIONS.append(Surface.ROTATION_0, 90);
          ORIENTATIONS.append(Surface.ROTATION_90, 0);
          ORIENTATIONS.append(Surface.ROTATION_180, 270);
          ORIENTATIONS.append(Surface.ROTATION_270, 180);
       * 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, Context context)
              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);
          // On most devices, the sensor orientation is 90 degrees, but for some
          // devices it is 270 degrees. For devices with a sensor orientation of
          // 270, rotate the image an additional 180 ((270 + 270) % 360) degrees.
          CameraManager cameraManager = (CameraManager) context.getSystemService(CAMERA_SERVICE);
          int sensorOrientation = cameraManager
          rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360;
          // Return the corresponding FirebaseVisionImageMetadata rotation value.
          int result;
          switch (rotationCompensation) {
              case 0:
                  result = FirebaseVisionImageMetadata.ROTATION_0;
              case 90:
                  result = FirebaseVisionImageMetadata.ROTATION_90;
              case 180:
                  result = FirebaseVisionImageMetadata.ROTATION_180;
              case 270:
                  result = FirebaseVisionImageMetadata.ROTATION_270;
                  result = FirebaseVisionImageMetadata.ROTATION_0;
                  Log.e(TAG, "Bad rotation value: " + rotationCompensation);
          return result;

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

      FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
    • To create a FirebaseVisionImage object from a ByteBuffer or a byte array, first calculate the image rotation as described above.

      Then, create a FirebaseVisionImageMetadata object that contains the image's height, width, color encoding format, and rotation:

      FirebaseVisionImageMetadata metadata = new FirebaseVisionImageMetadata.Builder()
              .setWidth(480)   // 480x360 is typically sufficient for
              .setHeight(360)  // image recognition

      Use the buffer or array, and the metadata object, to create a FirebaseVisionImage object:

      FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata);
      // Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
    • To create a FirebaseVisionImage object from a file, pass the app context and file URI to FirebaseVisionImage.fromFilePath():
      FirebaseVisionImage image;
      try {
          image = FirebaseVisionImage.fromFilePath(context, uri);
      } catch (IOException e) {

  2. Get an instance of FirebaseVisionBarcodeDetector:

    FirebaseVisionBarcodeDetector detector = FirebaseVision.getInstance()
    // Or, to specify the formats to recognize:
    // FirebaseVisionBarcodeDetector detector = FirebaseVision.getInstance()
    //        .getVisionBarcodeDetector(options);

  3. Finally, pass the image to the detectInImage method:

    Task<List<FirebaseVisionBarcode>> result = detector.detectInImage(image)
            .addOnSuccessListener(new OnSuccessListener<List<FirebaseVisionBarcode>>() {
                public void onSuccess(List<FirebaseVisionBarcode> barcodes) {
                    // Task completed successfully
                    // ...
            .addOnFailureListener(new OnFailureListener() {
                public void onFailure(@NonNull Exception e) {
                    // Task failed with an exception
                    // ...

3. Get information from barcodes

If the barcode recognition operation succeeds, a list of FirebaseVisionBarcode objects will be passed to the success listener. Each FirebaseVisionBarcode object represents a barcode that was detected in the image. For each barcode, you can get its bounding coordinates in the input image, as well as the raw data encoded by the barcode. Also, if the barcode detector was able to determine the type of data encoded by the barcode, you can get an object containing parsed data.

For example:

for (FirebaseVisionBarcode barcode: barcodes) {
    Rect bounds = barcode.getBoundingBox();
    Point[] corners = barcode.getCornerPoints();

    String rawValue = barcode.getRawValue();

    int valueType = barcode.getValueType();
    // See API reference for complete list of supported types
    switch (valueType) {
        case FirebaseVisionBarcode.TYPE_WIFI:
            String ssid = barcode.getWifi().getSsid();
            String password = barcode.getWifi().getPassword();
            int type = barcode.getWifi().getEncryptionType();
        case FirebaseVisionBarcode.TYPE_URL:
            String title = barcode.getUrl().getTitle();
            String url = barcode.getUrl().getUrl();

Tips to improve real-time performance

If you want to scan barcodes in a real-time application, follow these guidelines to achieve the best framerates:

  • Throttle calls to the detector. If a new video frame becomes available while the detector is running, drop the frame.
  • If you are using the output of the detector to overlay graphics on the input image, first get the result from ML Kit, then render the image and overlay in a single step. By doing so, you render 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.

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