iOS 13 Vision Text Document Scanner

Running through the new feature in iOS 13

Now that iOS 13 is here, the Vision API has vastly improved. Additionally, the VisionKit framework has been introduced, allowing us to scan documents using the new document camera.

Vision and VisionKit

Vision API came out with iOS 11. Up to now, it could only detect text and not return actual content, hence we had to bring in Core ML for the recognition part.

Now that the Vision API is upgraded with iOS 13, the VNRecognizedTextObservation returns the text, its confidence level, as well as the bounding box coordinates. Furthermore, VisionKit allows us to access the system’s document camera to scan pages.

VNDocumentCameraViewController is the view controller and VNDocumentCameraViewControllerDelegate is used to handle the delegate callbacks.

Launching a Document Camera

The following code is used to present the document camera on the screen.

let scannerViewController = VNDocumentCameraViewController() scannerViewController.delegate = self.present(scannerViewController, animated: true)

Once the scan is done and you just click Save andthe following delegate method gets triggered:

func documentCameraViewController(_ controller: VNDocumentCameraViewController, didFinishWith scan: VNDocumentCameraScan)

To get a particular scanned image among the multiple images, pass the index of the page in the method:scan.imageOfPage(at: index).

We can then process that image and detect the texts using the Vision API.

To process multiple images, we can loop through the scans in the delegate method like this:

for i in 0 ..< scan.pageCount {    let img = scan.imageOfPage(at: i)    processImage(img) }

Creating VNTextRecognitionRequest

let request = VNRecognizeTextRequest(completionHandler: nil) request.recognitionLevel = .accurate request.recognitionLanguages = ["en_US"]

The recognitionLevel could also be set to fast`, but then we'd have to deal with less accuracy.

recognitionLanguages is an array of languages passed in a priority order from left to right. We can also pass custom words that are not a part of the dictionary for Vision to recognize.

request.customWords = ["IOC", "COS"]

In the following section, let’s create a simple Xcode project in which we’ll recognize texts from the captured images using the Vision Request Handler. We’re setting our deployment target to iOS 13.

Our Storyboard


The code for ViewController.swift is given below:

import UIKit
import Vision
import VisionKit
class ViewController: UIViewController, VNDocumentCameraViewControllerDelegate {
@IBOutlet weak var imageView: UIImageView!
@IBOutlet weak var textView: UITextView!
var textRecognitionRequest = VNRecognizeTextRequest(completionHandler: nil)
private let textRecognitionWorkQueue = DispatchQueue(label: "MyVisionScannerQueue", qos: .userInitiated, attributes: [], autoreleaseFrequency: .workItem)
override func viewDidLoad() {
textView.isEditable = false
@IBAction func btnTakePicture(_ sender: Any) {
let scannerViewController = VNDocumentCameraViewController()
scannerViewController.delegate = self
present(scannerViewController, animated: true)
private func setupVision() {
textRecognitionRequest = VNRecognizeTextRequest { (request, error) in
guard let observations = request.results as? [VNRecognizedTextObservation] else { return }
var detectedText = ""
for observation in observations {
guard let topCandidate = observation.topCandidates(1).first else { return }
print("text \(topCandidate.string) has confidence \(topCandidate.confidence)")
detectedText += topCandidate.string
detectedText += "\n"
DispatchQueue.main.async {
self.textView.text = detectedText
textRecognitionRequest.recognitionLevel = .accurate
private func processImage(_ image: UIImage) {
imageView.image = image
private func recognizeTextInImage(_ image: UIImage) {
guard let cgImage = image.cgImage else { return }
textView.text = ""
textRecognitionWorkQueue.async {
let requestHandler = VNImageRequestHandler(cgImage: cgImage, options: [:])
do {
try requestHandler.perform([self.textRecognitionRequest])
} catch {
func documentCameraViewController(_ controller: VNDocumentCameraViewController, didFinishWith scan: VNDocumentCameraScan) {
guard scan.pageCount >= 1 else {
controller.dismiss(animated: true)
let originalImage = scan.imageOfPage(at: 0)
let newImage = compressedImage(originalImage)
controller.dismiss(animated: true)
func documentCameraViewController(_ controller: VNDocumentCameraViewController, didFailWithError error: Error) {
controller.dismiss(animated: true)
func documentCameraViewControllerDidCancel(_ controller: VNDocumentCameraViewController) {
controller.dismiss(animated: true)
func compressedImage(_ originalImage: UIImage) -> UIImage {
guard let imageData = originalImage.jpegData(compressionQuality: 1),
let reloadedImage = UIImage(data: imageData) else {
return originalImage
return reloadedImage

We’re doing quite a number of things in the above code. Let’s list them down.

The textRecognitionWorkQueue is a DispatchQueue used to run the vision request handler outside the main thread.

In the processImage function, we pass the image to a request handler which performs the text recognition.

VNRecognizedTextObservation is returned for each of the request's results.
From the VNRecognizedTextObservation, we can look up up to ten candidates. Typically, the top candidate gives us the most accurate result.

topCandidate.string returns the text and topCandidate.confidenceLevel returns us the confidence of the recognized text. To get the bounding box for the string in the image we can use this function:

topCandidate.boundingBox(for: topCandidate.string.startIndex..< topCandidate.string.endIndex)

This gives us CGRect, which we can draw over the image.

Vision uses a different coordinate space than UIKit, hence, when drawing the bounding boxes, you need to flip the y-axis.

Let’s look at the output of the application in action.

So, we just captured the cover of a bestselling novel and we were able to recognize to display the texts in a TextView on our screen. That sums up Vision text recognizer for iOS 13.

Browse the code from our Github Repository.