Identify text

The following APIs will allow you to identify text (words, tables, pages from a book) from an image.

For identifying text on iOS we use both AWS backend services as well as Apple's on-device Core ML Vision Framework to provide you with the most accurate results. If your device is offline, we will return results only from Core ML. On the other hand, if you are able to connect to AWS Services, we will return a unioned result from both the service and Core ML. Switching between backend services and Core ML is done automatically without any additional configuration required.

Set up the backend

If you haven't already done so, run amplify init inside your project and then amplify add auth (we recommend selecting the default configuration).

Run amplify add predictions, then use the following answers:

? Please select from one of the categories below
❯ Identify
  Convert
  Interpret
  Infer
  Learn More

? What would you like to identify? (Use arrow keys)
❯ Identify Text
  Identify Entities
  Identify Labels

? Provide a friendly name for your resource
    <Enter a friendly name here>

? Would you also like to identify documents?
    <Enter 'y'> 

? Who should have access?
  Auth users only
❯ Auth and Guest users

Run amplify push to create the resources in the cloud.

Identify text from image

Amplify will make calls to both Amazon Textract and Rekognition depending on the type of text you are looking to identify (i.e. image or document).

If you are detecting text from an image you would send in .plain as your text format as shown below. Using .plain with PredictionsIdentifyRequest.Options() combines results from on device results from Core ML and AWS services to yield more accurate results.

func detectText(_ image: URL, completion: @escaping ([IdentifiedWord]) -> Void) {
    Amplify.Predictions.identify(type: .detectText(.plain), image: image) { event in
        switch event {
        case let .success(result):
            let data = result as! IdentifyTextResult
            completion(data.words!)
        case let .failure(error):
            print(error)
        }
    }
}

func detectText(_ image: URL) -> AnyCancellable {
    Amplify.Predictions.identify(type: .detectText(.plain), image: image)
        .resultPublisher
        .sink {
            if case let .failure(error) = $0 {
                print(error)
            }
        }
        receiveValue: { result in
            let data = result as! IdentifyTextResult
            print(data.words)
        }
}

Note: Bounding boxes in IdentifyTextResult are returned as ratios. If you would like to place bounding boxes on individual recognized words that appear in the image, use the following method to calculate a frame for a single bounding box.

@IBAction func didTapButton(_ sender: Any) {
    let imageURL = URL(string: "https://imageWithText")
    let data = try? Data(contentsOf: imageURL!)
    let image = UIImage(data: data!)
    let imageView = UIImageView(image: image)
    self.view.addSubview(imageView)

    detectText(imageURL!, completion: { words in
        let word = words.first!
        DispatchQueue.main.async {
            let transform = CGAffineTransform(scaleX: imageView.frame.size.width, 
                                              y: imageView.frame.size.height)
            let boundingBox = UIView(frame: word.boundingBox.applying(transform))
            boundingBox.backgroundColor = .red
            imageView.addSubview(boundingBox)
        }
    })
}

Additionally it's important to note that Rekognition places (0,0) at the top left and Core ML places (0,0) at the bottom left. In order to handle this issue, you have flipped the y axis of the CoreML bounding box for you since iOS starts (0,0) from the top left.

To get results that utilize on-device capabilities (Core ML), without combining results from the backend, you can use the following to pass into the options argument of the Amplify.Predictions.identify function.

let options = PredictionsIdentifyRequest.Options(defaultNetworkPolicy: .offline, pluginOptions: nil)

Identify text in a document

Sending in .form or .table or .all will do document analysis as well as text detection to detect tables and forms in a document. See below for an example with .form.

func detectText(_ image: URL) {
    Amplify.Predictions.identify(type: .detectText(.form), image: image) { event in
        switch event {
        case let .success(result):
            let data = result as! IdentifyDocumentTextResult
            print(data)
        case let .failure(error):
            print(error)
        }
    }
}

func detectText(_ image: URL) -> AnyCancellable {
    Amplify.Predictions.identify(type: .detectText(.form), image: image)
        .resultPublisher
        .sink {
            if case let .failure(error) = $0 {
                print(error)
            }
        }
        receiveValue: { result in
            let data = result as! IdentifyDocumentTextResult
            print(data)
        }
}