This page describes an old version of the Text Recognition API, which was part of ML Kit for Firebase. The functionality of this API has been split into two new APIs (learn more):

• On-device text recognition is part of the new standalone ML Kit SDK, which you can use with or without Firebase.
• Cloud text recognition is part ofFirebase ML, which includes all of Firebase's cloud-based ML features.

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 theoverview for a comparison of the cloud and on-device models.

Before you begin

1. If you haven't already,add Firebase to your Android project.
2. Add the dependencies for the ML Kit Android libraries to your module (app-level) Gradle file (usuallyapp/build.gradle):

   applyplugin:'com.android.application'applyplugin:'com.google.gms.google-services'dependencies{// ...implementation'com.google.firebase:firebase-ml-vision:24.0.3'}

3. Optional but recommended: If you use the on-device API, 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'sAndroidManifest.xml file:

   <application...>...  <meta-dataandroid:name="com.google.firebase.ml.vision.DEPENDENCIES"android:value="ocr"/>  <!--Tousemultiplemodels: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.

4. If you want to use the Cloud-based model, and you have not already enabled the Cloud-based APIs for your project, do so now:

   1. Open theML Kit APIs page of theFirebase console.

   2. If you have not already upgraded your project to a Blaze pricing plan, clickUpgrade to do so. (You will be prompted to upgrade only if your project isn't on the Blaze plan.)

      Only Blaze-level projects can use Cloud-based APIs.

   3. If Cloud-based APIs aren't already enabled, clickEnable Cloud-based APIs.
   Before you deploy to production an app that uses a Cloud API, you should takesome additional steps toprevent and mitigate theeffect of unauthorized API access.

   If you want to use only the on-device model, you can skip this step.

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, for Latin text, each character should be at least 16x16 pixels. For Chinese, Japanese, and Korean text (only supported by the cloud-based APIs), each character should be 24x24 pixels. For all languages, 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 hurt 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 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 text occupies as much of the image as possible. Also seeTips to improve real-time performance.

Recognize text in images

To recognize text in an image using either an on-device or cloud-based model, run the text recognizer as described below.

1. Run the text recognizer

1. Create aFirebaseVisionImage object from your image.

   • To create aFirebaseVisionImage object from amedia.Image object, such as when capturing an image from a device's camera, pass themedia.Image object and the image's rotation toFirebaseVisionImage.fromMediaImage().

     If you use the CameraX library, theOnImageCapturedListener andImageAnalysis.Analyzer classes calculate the rotation value for you, so you just need to convert the rotation to one of ML Kit'sROTATION_ constants before callingFirebaseVisionImage.fromMediaImage():

     Java

     privateclassYourAnalyzerimplementsImageAnalysis.Analyzer{privateintdegreesToFirebaseRotation(intdegrees){switch(degrees){case0:returnFirebaseVisionImageMetadata.ROTATION_0;case90:returnFirebaseVisionImageMetadata.ROTATION_90;case180:returnFirebaseVisionImageMetadata.ROTATION_180;case270:returnFirebaseVisionImageMetadata.ROTATION_270;default:thrownewIllegalArgumentException("Rotation must be 0, 90, 180, or 270.");}}@Overridepublicvoidanalyze(ImageProxyimageProxy,intdegrees){if(imageProxy==null||imageProxy.getImage()==null){return;}ImagemediaImage=imageProxy.getImage();introtation=degreesToFirebaseRotation(degrees);FirebaseVisionImageimage=FirebaseVisionImage.fromMediaImage(mediaImage,rotation);// Pass image to an ML Kit Vision API// ...}}

     Kotlin

     privateclassYourImageAnalyzer:ImageAnalysis.Analyzer{privatefundegreesToFirebaseRotation(degrees:Int):Int=when(degrees){0->FirebaseVisionImageMetadata.ROTATION_090->FirebaseVisionImageMetadata.ROTATION_90180->FirebaseVisionImageMetadata.ROTATION_180270->FirebaseVisionImageMetadata.ROTATION_270else->throwException("Rotation must be 0, 90, 180, or 270.")}overridefunanalyze(imageProxy:ImageProxy?,degrees:Int){valmediaImage=imageProxy?.imagevalimageRotation=degreesToFirebaseRotation(degrees)if(mediaImage!=null){valimage=FirebaseVisionImage.fromMediaImage(mediaImage,imageRotation)// Pass image to an ML Kit Vision API// ...}}}

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

     Java

     privatestaticfinalSparseIntArrayORIENTATIONS=newSparseIntArray();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)privateintgetRotationCompensation(StringcameraId,Activityactivity,Contextcontext)throwsCameraAccessException{// 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.intdeviceRotation=activity.getWindowManager().getDefaultDisplay().getRotation();introtationCompensation=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.CameraManagercameraManager=(CameraManager)context.getSystemService(CAMERA_SERVICE);intsensorOrientation=cameraManager.getCameraCharacteristics(cameraId).get(CameraCharacteristics.SENSOR_ORIENTATION);rotationCompensation=(rotationCompensation+sensorOrientation+270)%360;// Return the corresponding FirebaseVisionImageMetadata rotation value.intresult;switch(rotationCompensation){case0:result=FirebaseVisionImageMetadata.ROTATION_0;break;case90:result=FirebaseVisionImageMetadata.ROTATION_90;break;case180:result=FirebaseVisionImageMetadata.ROTATION_180;break;case270:result=FirebaseVisionImageMetadata.ROTATION_270;break;default:result=FirebaseVisionImageMetadata.ROTATION_0;Log.e(TAG,"Bad rotation value: "+rotationCompensation);}returnresult;}
     VisionImage.java

     Kotlin

     privatevalORIENTATIONS=SparseIntArray()init{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)@Throws(CameraAccessException::class)privatefungetRotationCompensation(cameraId:String,activity:Activity,context:Context):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.valdeviceRotation=activity.windowManager.defaultDisplay.rotationvarrotationCompensation=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.valcameraManager=context.getSystemService(CAMERA_SERVICE)asCameraManagervalsensorOrientation=cameraManager.getCameraCharacteristics(cameraId).get(CameraCharacteristics.SENSOR_ORIENTATION)!!rotationCompensation=(rotationCompensation+sensorOrientation+270)%360// Return the corresponding FirebaseVisionImageMetadata rotation value.valresult:Intwhen(rotationCompensation){0->result=FirebaseVisionImageMetadata.ROTATION_090->result=FirebaseVisionImageMetadata.ROTATION_90180->result=FirebaseVisionImageMetadata.ROTATION_180270->result=FirebaseVisionImageMetadata.ROTATION_270else->{result=FirebaseVisionImageMetadata.ROTATION_0Log.e(TAG,"Bad rotation value:$rotationCompensation")}}returnresult}
     VisionImage.kt

     Then, pass themedia.Image object and the rotation value toFirebaseVisionImage.fromMediaImage():

     Java

     FirebaseVisionImageimage=FirebaseVisionImage.fromMediaImage(mediaImage,rotation);
     VisionImage.java

     Kotlin

     valimage=FirebaseVisionImage.fromMediaImage(mediaImage,rotation)
     VisionImage.kt

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

     Java

     FirebaseVisionImageimage;try{image=FirebaseVisionImage.fromFilePath(context,uri);}catch(IOExceptione){e.printStackTrace();}
     VisionImage.java

     Kotlin

     valimage:FirebaseVisionImagetry{image=FirebaseVisionImage.fromFilePath(context,uri)}catch(e:IOException){e.printStackTrace()}
     VisionImage.kt

   • To create aFirebaseVisionImage object from aByteBuffer or a byte array, first calculate the image rotation as described above formedia.Image input.

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

     Java

     FirebaseVisionImageMetadatametadata=newFirebaseVisionImageMetadata.Builder().setWidth(480)// 480x360 is typically sufficient for.setHeight(360)// image recognition.setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21).setRotation(rotation).build();
     VisionImage.java

     Kotlin

     valmetadata=FirebaseVisionImageMetadata.Builder().setWidth(480)// 480x360 is typically sufficient for.setHeight(360)// image recognition.setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21).setRotation(rotation).build()
     VisionImage.kt

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

     Java

     FirebaseVisionImageimage=FirebaseVisionImage.fromByteBuffer(buffer,metadata);// Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
     VisionImage.java

     Kotlin

     valimage=FirebaseVisionImage.fromByteBuffer(buffer,metadata)// Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)
     VisionImage.kt

   • To create aFirebaseVisionImage object from aBitmap object:

     Java

     FirebaseVisionImageimage=FirebaseVisionImage.fromBitmap(bitmap);
     VisionImage.java

     Kotlin

     valimage=FirebaseVisionImage.fromBitmap(bitmap)
     VisionImage.kt

     The image represented by theBitmap object must be upright, with no additional rotation required.

2. Get an instance ofFirebaseVisionTextRecognizer.

   To use the on-device model:

   Java

   FirebaseVisionTextRecognizerdetector=FirebaseVision.getInstance().getOnDeviceTextRecognizer();

   Kotlin

   valdetector=FirebaseVision.getInstance().onDeviceTextRecognizer

   To use the cloud-based model:

   Java

   FirebaseVisionTextRecognizerdetector=FirebaseVision.getInstance().getCloudTextRecognizer();// Or, to change the default settings://   FirebaseVisionTextRecognizer detector = FirebaseVision.getInstance()//          .getCloudTextRecognizer(options);

   // Or, to provide language hints to assist with language detection:// See https://cloud.google.com/vision/docs/languages for supported languagesFirebaseVisionCloudTextRecognizerOptionsoptions=newFirebaseVisionCloudTextRecognizerOptions.Builder().setLanguageHints(Arrays.asList("en","hi")).build();

   Kotlin

   valdetector=FirebaseVision.getInstance().cloudTextRecognizer// Or, to change the default settings:// val detector = FirebaseVision.getInstance().getCloudTextRecognizer(options)

   // Or, to provide language hints to assist with language detection:// See https://cloud.google.com/vision/docs/languages for supported languagesvaloptions=FirebaseVisionCloudTextRecognizerOptions.Builder().setLanguageHints(listOf("en","hi")).build()

   Use of ML Kit to access Cloud ML functionality is subject to theGoogle Cloud Platform LicenseAgreement andServiceSpecific Terms, and billed accordingly. For billing information, see theFirebasePricing page.

3. Finally, pass the image to theprocessImage method:

   Java

   Task<FirebaseVisionText>result=detector.processImage(image).addOnSuccessListener(newOnSuccessListener<FirebaseVisionText>(){@OverridepublicvoidonSuccess(FirebaseVisionTextfirebaseVisionText){// Task completed successfully// ...}}).addOnFailureListener(newOnFailureListener(){@OverridepublicvoidonFailure(@NonNullExceptione){// Task failed with an exception// ...}});

   Kotlin

   valresult=detector.processImage(image).addOnSuccessListener{firebaseVisionText->// Task completed successfully// ...}.addOnFailureListener{e->// Task failed with an exception// ...}

2. Extract text from blocks of recognized text

EachTextBlock represents a rectangular block of text, which contains zero ormoreLine objects. EachLine object contains zero or moreElement objects, which represent words and word-likeentities (dates, numbers, and so on).

For eachTextBlock,Line, andElement object, you can get the textrecognized in the region and the bounding coordinates of the region.

For example:

StringresultText=result.getText();for(FirebaseVisionText.TextBlockblock:result.getTextBlocks()){StringblockText=block.getText();FloatblockConfidence=block.getConfidence();List<RecognizedLanguage>blockLanguages=block.getRecognizedLanguages();Point[]blockCornerPoints=block.getCornerPoints();RectblockFrame=block.getBoundingBox();for(FirebaseVisionText.Lineline:block.getLines()){StringlineText=line.getText();FloatlineConfidence=line.getConfidence();List<RecognizedLanguage>lineLanguages=line.getRecognizedLanguages();Point[]lineCornerPoints=line.getCornerPoints();RectlineFrame=line.getBoundingBox();for(FirebaseVisionText.Elementelement:line.getElements()){StringelementText=element.getText();FloatelementConfidence=element.getConfidence();List<RecognizedLanguage>elementLanguages=element.getRecognizedLanguages();Point[]elementCornerPoints=element.getCornerPoints();RectelementFrame=element.getBoundingBox();}}}

valresultText=result.textfor(blockinresult.textBlocks){valblockText=block.textvalblockConfidence=block.confidencevalblockLanguages=block.recognizedLanguagesvalblockCornerPoints=block.cornerPointsvalblockFrame=block.boundingBoxfor(lineinblock.lines){vallineText=line.textvallineConfidence=line.confidencevallineLanguages=line.recognizedLanguagesvallineCornerPoints=line.cornerPointsvallineFrame=line.boundingBoxfor(elementinline.elements){valelementText=element.textvalelementConfidence=element.confidencevalelementLanguages=element.recognizedLanguagesvalelementCornerPoints=element.cornerPointsvalelementFrame=element.boundingBox}}}

Tips to improve real-time performance

If you want use the on-device model to recognize text in a real-time application, follow these guidelines to achieve the best framerates:

• Throttle calls to the text recognizer. If a new video frame becomes available while the text recognizer is running, drop the frame.
• If you are using 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. By doing so, you render to the display surface only once for each input frame.

• If you use the Camera2 API, capture images inImageFormat.YUV_420_888 format.

  If you use the older Camera API, capture images inImageFormat.NV21 format.

• Consider capturing images at a lower resolution. However, also keep in mind this API's image dimension requirements.

Next steps

• Before you deploy to production an app that uses a Cloud API, you should takesome additional steps toprevent and mitigate theeffect of unauthorized API access.

Recognize text in images of documents

To recognize the text of a document, configure and run the cloud-based document text recognizer as described below.

The document text recognition API, described below, provides an interface thatis intended to be more convenient for working with images of documents. However,if you prefer the interface provided by theFirebaseVisionTextRecognizer API,you can use it instead to scan documents by configuring the cloud textrecognizer touse the dense text model.

To use the document text recognition API:

1. Run the text recognizer

1. Create aFirebaseVisionImage object from your image.

   • To create aFirebaseVisionImage object from amedia.Image object, such as when capturing an image from a device's camera, pass themedia.Image object and the image's rotation toFirebaseVisionImage.fromMediaImage().

     If you use the CameraX library, theOnImageCapturedListener andImageAnalysis.Analyzer classes calculate the rotation value for you, so you just need to convert the rotation to one of ML Kit'sROTATION_ constants before callingFirebaseVisionImage.fromMediaImage():

     Java

     privateclassYourAnalyzerimplementsImageAnalysis.Analyzer{privateintdegreesToFirebaseRotation(intdegrees){switch(degrees){case0:returnFirebaseVisionImageMetadata.ROTATION_0;case90:returnFirebaseVisionImageMetadata.ROTATION_90;case180:returnFirebaseVisionImageMetadata.ROTATION_180;case270:returnFirebaseVisionImageMetadata.ROTATION_270;default:thrownewIllegalArgumentException("Rotation must be 0, 90, 180, or 270.");}}@Overridepublicvoidanalyze(ImageProxyimageProxy,intdegrees){if(imageProxy==null||imageProxy.getImage()==null){return;}ImagemediaImage=imageProxy.getImage();introtation=degreesToFirebaseRotation(degrees);FirebaseVisionImageimage=FirebaseVisionImage.fromMediaImage(mediaImage,rotation);// Pass image to an ML Kit Vision API// ...}}

     Kotlin

     privateclassYourImageAnalyzer:ImageAnalysis.Analyzer{privatefundegreesToFirebaseRotation(degrees:Int):Int=when(degrees){0->FirebaseVisionImageMetadata.ROTATION_090->FirebaseVisionImageMetadata.ROTATION_90180->FirebaseVisionImageMetadata.ROTATION_180270->FirebaseVisionImageMetadata.ROTATION_270else->throwException("Rotation must be 0, 90, 180, or 270.")}overridefunanalyze(imageProxy:ImageProxy?,degrees:Int){valmediaImage=imageProxy?.imagevalimageRotation=degreesToFirebaseRotation(degrees)if(mediaImage!=null){valimage=FirebaseVisionImage.fromMediaImage(mediaImage,imageRotation)// Pass image to an ML Kit Vision API// ...}}}

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

     Java

     privatestaticfinalSparseIntArrayORIENTATIONS=newSparseIntArray();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)privateintgetRotationCompensation(StringcameraId,Activityactivity,Contextcontext)throwsCameraAccessException{// 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.intdeviceRotation=activity.getWindowManager().getDefaultDisplay().getRotation();introtationCompensation=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.CameraManagercameraManager=(CameraManager)context.getSystemService(CAMERA_SERVICE);intsensorOrientation=cameraManager.getCameraCharacteristics(cameraId).get(CameraCharacteristics.SENSOR_ORIENTATION);rotationCompensation=(rotationCompensation+sensorOrientation+270)%360;// Return the corresponding FirebaseVisionImageMetadata rotation value.intresult;switch(rotationCompensation){case0:result=FirebaseVisionImageMetadata.ROTATION_0;break;case90:result=FirebaseVisionImageMetadata.ROTATION_90;break;case180:result=FirebaseVisionImageMetadata.ROTATION_180;break;case270:result=FirebaseVisionImageMetadata.ROTATION_270;break;default:result=FirebaseVisionImageMetadata.ROTATION_0;Log.e(TAG,"Bad rotation value: "+rotationCompensation);}returnresult;}
     VisionImage.java

     Kotlin

     privatevalORIENTATIONS=SparseIntArray()init{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)@Throws(CameraAccessException::class)privatefungetRotationCompensation(cameraId:String,activity:Activity,context:Context):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.valdeviceRotation=activity.windowManager.defaultDisplay.rotationvarrotationCompensation=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.valcameraManager=context.getSystemService(CAMERA_SERVICE)asCameraManagervalsensorOrientation=cameraManager.getCameraCharacteristics(cameraId).get(CameraCharacteristics.SENSOR_ORIENTATION)!!rotationCompensation=(rotationCompensation+sensorOrientation+270)%360// Return the corresponding FirebaseVisionImageMetadata rotation value.valresult:Intwhen(rotationCompensation){0->result=FirebaseVisionImageMetadata.ROTATION_090->result=FirebaseVisionImageMetadata.ROTATION_90180->result=FirebaseVisionImageMetadata.ROTATION_180270->result=FirebaseVisionImageMetadata.ROTATION_270else->{result=FirebaseVisionImageMetadata.ROTATION_0Log.e(TAG,"Bad rotation value:$rotationCompensation")}}returnresult}
     VisionImage.kt

     Then, pass themedia.Image object and the rotation value toFirebaseVisionImage.fromMediaImage():

     Java

     FirebaseVisionImageimage=FirebaseVisionImage.fromMediaImage(mediaImage,rotation);
     VisionImage.java

     Kotlin

     valimage=FirebaseVisionImage.fromMediaImage(mediaImage,rotation)
     VisionImage.kt

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

     Java

     FirebaseVisionImageimage;try{image=FirebaseVisionImage.fromFilePath(context,uri);}catch(IOExceptione){e.printStackTrace();}
     VisionImage.java

     Kotlin

     valimage:FirebaseVisionImagetry{image=FirebaseVisionImage.fromFilePath(context,uri)}catch(e:IOException){e.printStackTrace()}
     VisionImage.kt

   • To create aFirebaseVisionImage object from aByteBuffer or a byte array, first calculate the image rotation as described above formedia.Image input.

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

     Java

     FirebaseVisionImageMetadatametadata=newFirebaseVisionImageMetadata.Builder().setWidth(480)// 480x360 is typically sufficient for.setHeight(360)// image recognition.setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21).setRotation(rotation).build();
     VisionImage.java

     Kotlin

     valmetadata=FirebaseVisionImageMetadata.Builder().setWidth(480)// 480x360 is typically sufficient for.setHeight(360)// image recognition.setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21).setRotation(rotation).build()
     VisionImage.kt

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

     Java

     FirebaseVisionImageimage=FirebaseVisionImage.fromByteBuffer(buffer,metadata);// Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
     VisionImage.java

     Kotlin

     valimage=FirebaseVisionImage.fromByteBuffer(buffer,metadata)// Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)
     VisionImage.kt

   • To create aFirebaseVisionImage object from aBitmap object:

     Java

     FirebaseVisionImageimage=FirebaseVisionImage.fromBitmap(bitmap);
     VisionImage.java

     Kotlin

     valimage=FirebaseVisionImage.fromBitmap(bitmap)
     VisionImage.kt

     The image represented by theBitmap object must be upright, with no additional rotation required.

2. Get an instance ofFirebaseVisionDocumentTextRecognizer:

   Java

   FirebaseVisionDocumentTextRecognizerdetector=FirebaseVision.getInstance().getCloudDocumentTextRecognizer();

   // Or, to provide language hints to assist with language detection:// See https://cloud.google.com/vision/docs/languages for supported languagesFirebaseVisionCloudDocumentRecognizerOptionsoptions=newFirebaseVisionCloudDocumentRecognizerOptions.Builder().setLanguageHints(Arrays.asList("en","hi")).build();FirebaseVisionDocumentTextRecognizerdetector=FirebaseVision.getInstance().getCloudDocumentTextRecognizer(options);

   Kotlin

   valdetector=FirebaseVision.getInstance().cloudDocumentTextRecognizer

   // Or, to provide language hints to assist with language detection:// See https://cloud.google.com/vision/docs/languages for supported languagesvaloptions=FirebaseVisionCloudDocumentRecognizerOptions.Builder().setLanguageHints(listOf("en","hi")).build()valdetector=FirebaseVision.getInstance().getCloudDocumentTextRecognizer(options)

3. Finally, pass the image to theprocessImage method:

   Java

   detector.processImage(myImage).addOnSuccessListener(newOnSuccessListener<FirebaseVisionDocumentText>(){@OverridepublicvoidonSuccess(FirebaseVisionDocumentTextresult){// Task completed successfully// ...}}).addOnFailureListener(newOnFailureListener(){@OverridepublicvoidonFailure(@NonNullExceptione){// Task failed with an exception// ...}});

   Kotlin

   detector.processImage(myImage).addOnSuccessListener{firebaseVisionDocumentText->// Task completed successfully// ...}.addOnFailureListener{e->// Task failed with an exception// ...}

2. Extract text from blocks of recognized text

If the text recognition operation succeeds, it will return aFirebaseVisionDocumentText object. AFirebaseVisionDocumentText object contains the full text recognized in theimage and a hierarchy of objects that reflect the structure of the recognizeddocument:

• FirebaseVisionDocumentText.Block
• FirebaseVisionDocumentText.Paragraph
• FirebaseVisionDocumentText.Word
• FirebaseVisionDocumentText.Symbol

For eachBlock,Paragraph,Word, andSymbol object, you can get thetext recognized in the region and the bounding coordinates of the region.

For example:

StringresultText=result.getText();for(FirebaseVisionDocumentText.Blockblock:result.getBlocks()){StringblockText=block.getText();FloatblockConfidence=block.getConfidence();List<RecognizedLanguage>blockRecognizedLanguages=block.getRecognizedLanguages();RectblockFrame=block.getBoundingBox();for(FirebaseVisionDocumentText.Paragraphparagraph:block.getParagraphs()){StringparagraphText=paragraph.getText();FloatparagraphConfidence=paragraph.getConfidence();List<RecognizedLanguage>paragraphRecognizedLanguages=paragraph.getRecognizedLanguages();RectparagraphFrame=paragraph.getBoundingBox();for(FirebaseVisionDocumentText.Wordword:paragraph.getWords()){StringwordText=word.getText();FloatwordConfidence=word.getConfidence();List<RecognizedLanguage>wordRecognizedLanguages=word.getRecognizedLanguages();RectwordFrame=word.getBoundingBox();for(FirebaseVisionDocumentText.Symbolsymbol:word.getSymbols()){StringsymbolText=symbol.getText();FloatsymbolConfidence=symbol.getConfidence();List<RecognizedLanguage>symbolRecognizedLanguages=symbol.getRecognizedLanguages();RectsymbolFrame=symbol.getBoundingBox();}}}}

valresultText=result.textfor(blockinresult.blocks){valblockText=block.textvalblockConfidence=block.confidencevalblockRecognizedLanguages=block.recognizedLanguagesvalblockFrame=block.boundingBoxfor(paragraphinblock.paragraphs){valparagraphText=paragraph.textvalparagraphConfidence=paragraph.confidencevalparagraphRecognizedLanguages=paragraph.recognizedLanguagesvalparagraphFrame=paragraph.boundingBoxfor(wordinparagraph.words){valwordText=word.textvalwordConfidence=word.confidencevalwordRecognizedLanguages=word.recognizedLanguagesvalwordFrame=word.boundingBoxfor(symbolinword.symbols){valsymbolText=symbol.textvalsymbolConfidence=symbol.confidencevalsymbolRecognizedLanguages=symbol.recognizedLanguagesvalsymbolFrame=symbol.boundingBox}}}}

Next steps

• Before you deploy to production an app that uses a Cloud API, you should takesome additional steps toprevent and mitigate theeffect of unauthorized API access.