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 the overview for a comparison of the cloud and on-device models.
Before you begin
- If you have not already added Firebase to your app, do so by following the steps in the getting started guide .
- Include the ML Kit libraries in your Podfile:
pod 'Firebase/MLVision', '6.25.0' # If using an on-device API: pod 'Firebase/MLVisionTextModel', '6.25.0'
.xcworkspace. - In your app, import Firebase:
Swift
import Firebase
Objective-C
@import Firebase ;
-
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:
- Open the ML Kit APIs page of the Firebase console.
-
If you have not already upgraded your project to a Blaze pricing plan, click Upgrade 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.
- If Cloud-based APIs aren't already enabled, click Enable Cloud-based APIs .
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 see Tips 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
Pass the image as a `UIImage` or a `CMSampleBufferRef` to the `VisionTextRecognizer`'s `process(_:completion:)` method:- Get an instance of
VisionTextRecognizerby calling eitheronDeviceTextRecognizerorcloudTextRecognizer:Swift
To use the on-device model:
let vision = Vision . vision () let textRecognizer = vision . onDeviceTextRecognizer ()
To use the cloud model:
let vision = Vision . vision () let textRecognizer = vision . cloudTextRecognizer () // Or, to provide language hints to assist with language detection: // See https://cloud.google.com/vision/docs/languages for supported languages let options = VisionCloudTextRecognizerOptions () options . languageHints = [ "en" , "hi" ] let textRecognizer = vision . cloudTextRecognizer ( options : options )
Objective-C
To use the on-device model:
FIRVision * vision = [ FIRVision vision ]; FIRVisionTextRecognizer * textRecognizer = [ vision onDeviceTextRecognizer ];
To use the cloud model:
FIRVision * vision = [ FIRVision vision ]; FIRVisionTextRecognizer * textRecognizer = [ vision cloudTextRecognizer ]; // Or, to provide language hints to assist with language detection: // See https://cloud.google.com/vision/docs/languages for supported languages FIRVisionCloudTextRecognizerOptions * options = [[ FIRVisionCloudTextRecognizerOptions alloc ] init ]; options . languageHints = @[ @"en" , @"hi" ] ; FIRVisionTextRecognizer * textRecognizer = [ vision cloudTextRecognizerWithOptions : options ];
-
Create a
VisionImageobject using aUIImageor aCMSampleBufferRef.To use a
UIImage:- If necessary, rotate the image so that its
imageOrientationproperty is.up. - Create a
VisionImageobject using the correctly-rotatedUIImage. Do not specify any rotation metadata—the default value,.topLeft, must be used.Swift
let image = VisionImage ( image : uiImage )
Objective-C
FIRVisionImage * image = [[ FIRVisionImage alloc ] initWithImage : uiImage ];
To use a
CMSampleBufferRef:-
Create a
VisionImageMetadataobject that specifies the orientation of the image data contained in theCMSampleBufferRefbuffer.To get the image orientation:
Swift
func imageOrientation ( deviceOrientation : UIDeviceOrientation , cameraPosition : AVCaptureDevice . Position ) - > VisionDetectorImageOrientation { switch deviceOrientation { case . portrait : return cameraPosition == . front ? . leftTop : . rightTop case . landscapeLeft : return cameraPosition == . front ? . bottomLeft : . topLeft case . portraitUpsideDown : return cameraPosition == . front ? . rightBottom : . leftBottom case . landscapeRight : return cameraPosition == . front ? . topRight : . bottomRight case . faceDown , . faceUp , . unknown : return . leftTop } }
Objective-C
- ( FIRVisionDetectorImageOrientation ) imageOrientationFromDeviceOrientation: ( UIDeviceOrientation ) deviceOrientation cameraPosition :( AVCaptureDevicePosition ) cameraPosition { switch ( deviceOrientation ) { case UIDeviceOrientationPortrait : if ( cameraPosition == AVCaptureDevicePositionFront ) { return FIRVisionDetectorImageOrientationLeftTop ; } else { return FIRVisionDetectorImageOrientationRightTop ; } case UIDeviceOrientationLandscapeLeft : if ( cameraPosition == AVCaptureDevicePositionFront ) { return FIRVisionDetectorImageOrientationBottomLeft ; } else { return FIRVisionDetectorImageOrientationTopLeft ; } case UIDeviceOrientationPortraitUpsideDown : if ( cameraPosition == AVCaptureDevicePositionFront ) { return FIRVisionDetectorImageOrientationRightBottom ; } else { return FIRVisionDetectorImageOrientationLeftBottom ; } case UIDeviceOrientationLandscapeRight : if ( cameraPosition == AVCaptureDevicePositionFront ) { return FIRVisionDetectorImageOrientationTopRight ; } else { return FIRVisionDetectorImageOrientationBottomRight ; } default : return FIRVisionDetectorImageOrientationTopLeft ; } }
Then, create the metadata object:
Swift
let cameraPosition = AVCaptureDevice . Position . back // Set to the capture device you used. let metadata = VisionImageMetadata () metadata . orientation = imageOrientation ( deviceOrientation : UIDevice . current . orientation , cameraPosition : cameraPosition )
Objective-C
FIRVisionImageMetadata * metadata = [[ FIRVisionImageMetadata alloc ] init ]; AVCaptureDevicePosition cameraPosition = AVCaptureDevicePositionBack ; // Set to the capture device you used. metadata . orientation = [ self imageOrientationFromDeviceOrientation : UIDevice . currentDevice . orientation cameraPosition : cameraPosition ];
- Create a
VisionImageobject using theCMSampleBufferRefobject and the rotation metadata:Swift
let image = VisionImage ( buffer : sampleBuffer ) image . metadata = metadata
Objective-C
FIRVisionImage * image = [[ FIRVisionImage alloc ] initWithBuffer : sampleBuffer ]; image . metadata = metadata ;
- If necessary, rotate the image so that its
- Then, pass the image to the
process(_:completion:)method:Swift
textRecognizer . process ( visionImage ) { result , error in guard error == nil , let result = result else { // ... return } // Recognized text }
Objective-C
[ textRecognizer processImage : image completion : ^ ( FIRVisionText * _Nullable result , NSError * _Nullable error ) { if ( error != nil || result == nil ) { // ... return ; } // Recognized text }];
2. Extract text from blocks of recognized text
If the text recognition operation succeeds, it will return a [`VisionText`][VisionText] object. A `VisionText` object contains the full text recognized in the image and zero or more [`VisionTextBlock`][VisionTextBlock] objects. Each `VisionTextBlock` represents a rectangular block of text, which contain zero or more [`VisionTextLine`][VisionTextLine] objects. Each `VisionTextLine` object contains zero or more [`VisionTextElement`][VisionTextElement] objects, which represent words and word-like entities (dates, numbers, and so on). For each `VisionTextBlock`, `VisionTextLine`, and `VisionTextElement` object, you can get the text recognized in the region and the bounding coordinates of the region. For example:Swift
let resultText = result . text for block in result . blocks { let blockText = block . text let blockConfidence = block . confidence let blockLanguages = block . recognizedLanguages let blockCornerPoints = block . cornerPoints let blockFrame = block . frame for line in block . lines { let lineText = line . text let lineConfidence = line . confidence let lineLanguages = line . recognizedLanguages let lineCornerPoints = line . cornerPoints let lineFrame = line . frame for element in line . elements { let elementText = element . text let elementConfidence = element . confidence let elementLanguages = element . recognizedLanguages let elementCornerPoints = element . cornerPoints let elementFrame = element . frame } } }
Objective-C
NSString * resultText = result . text ; for ( FIRVisionTextBlock * block in result . blocks ) { NSString * blockText = block . text ; NSNumber * blockConfidence = block . confidence ; NSArray<FIRVisionTextRecognizedLanguage *> * blockLanguages = block . recognizedLanguages ; NSArray<NSValue *> * blockCornerPoints = block . cornerPoints ; CGRect blockFrame = block . frame ; for ( FIRVisionTextLine * line in block . lines ) { NSString * lineText = line . text ; NSNumber * lineConfidence = line . confidence ; NSArray<FIRVisionTextRecognizedLanguage *> * lineLanguages = line . recognizedLanguages ; NSArray<NSValue *> * lineCornerPoints = line . cornerPoints ; CGRect lineFrame = line . frame ; for ( FIRVisionTextElement * element in line . elements ) { NSString * elementText = element . text ; NSNumber * elementConfidence = element . confidence ; NSArray<FIRVisionTextRecognizedLanguage *> * elementLanguages = element . recognizedLanguages ; NSArray<NSValue *> * elementCornerPoints = element . cornerPoints ; CGRect elementFrame = element . frame ; } } }
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. See the previewOverlayView and FIRDetectionOverlayView classes in the showcase sample app for an example.
- 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 take some additional steps to prevent and mitigate the effect 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 that is intended to be more convenient for working with images of documents. However, if you prefer the interface provided by the sparse text API, you can use it instead to scan documents by configuring the cloud text recognizer to use the dense text model .
To use the document text recognition API:
1. Run the text recognizer
Pass the image as aUIImage
or a CMSampleBufferRef
to the VisionDocumentTextRecognizer
's process(_:completion:)
method: - Get an instance of
VisionDocumentTextRecognizerby callingcloudDocumentTextRecognizer:Swift
let vision = Vision . vision () let textRecognizer = vision . cloudDocumentTextRecognizer () // Or, to provide language hints to assist with language detection: // See https://cloud.google.com/vision/docs/languages for supported languages let options = VisionCloudDocumentTextRecognizerOptions () options . languageHints = [ "en" , "hi" ] let textRecognizer = vision . cloudDocumentTextRecognizer ( options : options )
Objective-C
FIRVision * vision = [ FIRVision vision ]; FIRVisionDocumentTextRecognizer * textRecognizer = [ vision cloudDocumentTextRecognizer ]; // Or, to provide language hints to assist with language detection: // See https://cloud.google.com/vision/docs/languages for supported languages FIRVisionCloudDocumentTextRecognizerOptions * options = [[ FIRVisionCloudDocumentTextRecognizerOptions alloc ] init ]; options . languageHints = @[ @"en" , @"hi" ] ; FIRVisionDocumentTextRecognizer * textRecognizer = [ vision cloudDocumentTextRecognizerWithOptions : options ];
-
Create a
VisionImageobject using aUIImageor aCMSampleBufferRef.To use a
UIImage:- If necessary, rotate the image so that its
imageOrientationproperty is.up. - Create a
VisionImageobject using the correctly-rotatedUIImage. Do not specify any rotation metadata—the default value,.topLeft, must be used.Swift
let image = VisionImage ( image : uiImage )
Objective-C
FIRVisionImage * image = [[ FIRVisionImage alloc ] initWithImage : uiImage ];
To use a
CMSampleBufferRef:-
Create a
VisionImageMetadataobject that specifies the orientation of the image data contained in theCMSampleBufferRefbuffer.To get the image orientation:
Swift
func imageOrientation ( deviceOrientation : UIDeviceOrientation , cameraPosition : AVCaptureDevice . Position ) - > VisionDetectorImageOrientation { switch deviceOrientation { case . portrait : return cameraPosition == . front ? . leftTop : . rightTop case . landscapeLeft : return cameraPosition == . front ? . bottomLeft : . topLeft case . portraitUpsideDown : return cameraPosition == . front ? . rightBottom : . leftBottom case . landscapeRight : return cameraPosition == . front ? . topRight : . bottomRight case . faceDown , . faceUp , . unknown : return . leftTop } }
Objective-C
- ( FIRVisionDetectorImageOrientation ) imageOrientationFromDeviceOrientation: ( UIDeviceOrientation ) deviceOrientation cameraPosition :( AVCaptureDevicePosition ) cameraPosition { switch ( deviceOrientation ) { case UIDeviceOrientationPortrait : if ( cameraPosition == AVCaptureDevicePositionFront ) { return FIRVisionDetectorImageOrientationLeftTop ; } else { return FIRVisionDetectorImageOrientationRightTop ; } case UIDeviceOrientationLandscapeLeft : if ( cameraPosition == AVCaptureDevicePositionFront ) { return FIRVisionDetectorImageOrientationBottomLeft ; } else { return FIRVisionDetectorImageOrientationTopLeft ; } case UIDeviceOrientationPortraitUpsideDown : if ( cameraPosition == AVCaptureDevicePositionFront ) { return FIRVisionDetectorImageOrientationRightBottom ; } else { return FIRVisionDetectorImageOrientationLeftBottom ; } case UIDeviceOrientationLandscapeRight : if ( cameraPosition == AVCaptureDevicePositionFront ) { return FIRVisionDetectorImageOrientationTopRight ; } else { return FIRVisionDetectorImageOrientationBottomRight ; } default : return FIRVisionDetectorImageOrientationTopLeft ; } }
Then, create the metadata object:
Swift
let cameraPosition = AVCaptureDevice . Position . back // Set to the capture device you used. let metadata = VisionImageMetadata () metadata . orientation = imageOrientation ( deviceOrientation : UIDevice . current . orientation , cameraPosition : cameraPosition )
Objective-C
FIRVisionImageMetadata * metadata = [[ FIRVisionImageMetadata alloc ] init ]; AVCaptureDevicePosition cameraPosition = AVCaptureDevicePositionBack ; // Set to the capture device you used. metadata . orientation = [ self imageOrientationFromDeviceOrientation : UIDevice . currentDevice . orientation cameraPosition : cameraPosition ];
- Create a
VisionImageobject using theCMSampleBufferRefobject and the rotation metadata:Swift
let image = VisionImage ( buffer : sampleBuffer ) image . metadata = metadata
Objective-C
FIRVisionImage * image = [[ FIRVisionImage alloc ] initWithBuffer : sampleBuffer ]; image . metadata = metadata ;
- If necessary, rotate the image so that its
- Then, pass the image to the
process(_:completion:)method:Swift
textRecognizer . process ( visionImage ) { result , error in guard error == nil , let result = result else { // ... return } // Recognized text }
Objective-C
[ textRecognizer processImage : image completion : ^ ( FIRVisionDocumentText * _Nullable result , NSError * _Nullable error ) { if ( error != nil || result == nil ) { // ... return ; } // Recognized text }];
2. Extract text from blocks of recognized text
If the text recognition operation succeeds, it will return aVisionDocumentText
object. A VisionDocumentText
object
contains the full text recognized in the image and a hierarchy of objects that
reflect the structure of the recognized document:
For each VisionDocumentTextBlock
, VisionDocumentTextParagraph
, VisionDocumentTextWord
, and VisionDocumentTextSymbol
object, you can get the
text recognized in the region and the bounding coordinates of the region.
For example:
Swift
let resultText = result . text for block in result . blocks { let blockText = block . text let blockConfidence = block . confidence let blockRecognizedLanguages = block . recognizedLanguages let blockBreak = block . recognizedBreak let blockCornerPoints = block . cornerPoints let blockFrame = block . frame for paragraph in block . paragraphs { let paragraphText = paragraph . text let paragraphConfidence = paragraph . confidence let paragraphRecognizedLanguages = paragraph . recognizedLanguages let paragraphBreak = paragraph . recognizedBreak let paragraphCornerPoints = paragraph . cornerPoints let paragraphFrame = paragraph . frame for word in paragraph . words { let wordText = word . text let wordConfidence = word . confidence let wordRecognizedLanguages = word . recognizedLanguages let wordBreak = word . recognizedBreak let wordCornerPoints = word . cornerPoints let wordFrame = word . frame for symbol in word . symbols { let symbolText = symbol . text let symbolConfidence = symbol . confidence let symbolRecognizedLanguages = symbol . recognizedLanguages let symbolBreak = symbol . recognizedBreak let symbolCornerPoints = symbol . cornerPoints let symbolFrame = symbol . frame } } } }
Objective-C
NSString * resultText = result . text ; for ( FIRVisionDocumentTextBlock * block in result . blocks ) { NSString * blockText = block . text ; NSNumber * blockConfidence = block . confidence ; NSArray<FIRVisionTextRecognizedLanguage *> * blockRecognizedLanguages = block . recognizedLanguages ; FIRVisionTextRecognizedBreak * blockBreak = block . recognizedBreak ; CGRect blockFrame = block . frame ; for ( FIRVisionDocumentTextParagraph * paragraph in block . paragraphs ) { NSString * paragraphText = paragraph . text ; NSNumber * paragraphConfidence = paragraph . confidence ; NSArray<FIRVisionTextRecognizedLanguage *> * paragraphRecognizedLanguages = paragraph . recognizedLanguages ; FIRVisionTextRecognizedBreak * paragraphBreak = paragraph . recognizedBreak ; CGRect paragraphFrame = paragraph . frame ; for ( FIRVisionDocumentTextWord * word in paragraph . words ) { NSString * wordText = word . text ; NSNumber * wordConfidence = word . confidence ; NSArray<FIRVisionTextRecognizedLanguage *> * wordRecognizedLanguages = word . recognizedLanguages ; FIRVisionTextRecognizedBreak * wordBreak = word . recognizedBreak ; CGRect wordFrame = word . frame ; for ( FIRVisionDocumentTextSymbol * symbol in word . symbols ) { NSString * symbolText = symbol . text ; NSNumber * symbolConfidence = symbol . confidence ; NSArray<FIRVisionTextRecognizedLanguage *> * symbolRecognizedLanguages = symbol . recognizedLanguages ; FIRVisionTextRecognizedBreak * symbolBreak = symbol . recognizedBreak ; CGRect symbolFrame = symbol . frame ; } } } }
Next steps
- Before you deploy to production an app that uses a Cloud API, you should take some additional steps to prevent and mitigate the effect of unauthorized API access .
