We created a mlmodel with playground like https://developer.apple.com/documentation/createml/creating_an_image_classifier_model.
Then we used following code to get bounding box data of objects in that mlmodel. But in "results" we can get just prediction values and object names we modeled, even that was exiting but not our aim.
print("detectOurModelHandler (results)") Shows us the all the objects and
prediction values in our mlmodel and it is VNClassificationObservation.
So it is no surprise that we do not have box data.
So the problem is how to create model as VNRecognizedObjectObservation, I think ?
According to https://developer.apple.com/documentation/vision/recognizing_objects_in_live_capture we are supposed to get bounding box data.
But we can not. Even print("detectOurModelHandler 2") is never called like dump(objectBounds).
We call findOurModels in captureOutput by the way. We call it like once in 1 second to test our model at the moment.
lazy var ourModel:VNCoreMLModel = { return try! VNCoreMLModel(for: ImageClassifier().model)}()
lazy var ourModelRequest: VNCoreMLRequest = {
return VNCoreMLRequest(model: ourModel, completionHandler: detectOutModelHandler)
}()
func findOurModels(pixelbuffer: CVPixelBuffer){
let testImage = takeAFrameImage(imageBuffer: pixelbuffer)
let imageForThis = testImage.cgImage
let requestOptions2:[VNImageOption : Any] = [:]
let handler = VNImageRequestHandler(cgImage: imageForThis!,
orientation: CGImagePropertyOrientation(rawValue: 6)!,
options: requestOptions2)
try? handler.perform([ourModelRequest])
}
func detectOurModelHandler(request: VNRequest, error: Error?) {
DispatchQueue.main.async(execute: {
if let results = request.results {
print("detectOurModelHandler \(results)")
for observation in results where observation is VNRecognizedObjectObservation {
print("detectOurModelHandler 2")
guard let objectObservation = observation as? VNRecognizedObjectObservation else {
continue
}
let objectBounds = VNImageRectForNormalizedRect(objectObservation.boundingBox, self.frameWidth, self.frameHeight)
dump(objectBounds)
}
}
})
}
It can not be done using CreateML.
I did not do it yet but it is said a model with bounding data could be created with Turi Create.
Related
I have some code that reads data from Firebase on a custom loading screen that I only want to segue once all of the data in the collection has been read (I know beforehand that there won't be more than 10 or 15 data entries to read, and I'm checking to make sure the user has an internet connection). I have a loading animation I'd like to implement that is started by calling activityIndicatorView.startAnimating() and stopped by calling activityIndicatorView.stopAnimating(). I'm not sure where to place these or the perform segue function in relation to the data retrieval function. Any help is appreciated!
let db = Firestore.firestore()
db.collection("Packages").getDocuments{(snapshot, error) in
if error != nil{
// DB error
} else{
for doc in snapshot!.documents{
self.packageIDS.append(doc.documentID)
self.packageNames.append(doc.get("title") as! String)
self.packageIMGIDS.append(doc.get("imgID") as! String)
self.packageRadii.append(doc.get("radius") as! String)
}
}
}
You don't need to know the progress of the read as such, just when it starts and when it is complete, so that you can start and stop your activity view.
The read starts when you call getDocuments.
The read is complete after the for loop in the getDocuments completion closure.
So:
let db = Firestore.firestore()
activityIndicatorView.startAnimating()
db.collection("Packages").getDocuments{(snapshot, error) in
if error != nil{
// DB error
} else {
for doc in snapshot!.documents{
self.packageIDS.append(doc.documentID)
self.packageNames.append(doc.get("title") as! String)
self.packageIMGIDS.append(doc.get("imgID") as! String)
self.packageRadii.append(doc.get("radius") as! String)
}
}
DispatchQueue.main.async {
activityIndicatorView.stopAnimating()
}
}
As a matter of style, having multiple arrays with associate data is a bit of a code smell. Rather you should create a struct with the relevant properties and create a single array of instances of this struct.
You should also avoid force unwrapping.
struct PackageInfo {
let id: String
let name: String
let imageId: String
let radius: String
}
...
var packages:[PackageInfo] = []
...
db.collection("Packages").getDocuments{(snapshot, error) in
if error != nil{
// DB error
} else if let documents = snapshot?.documents {
self.packages = documents.compactMap { doc in
if let title = doc.get("title") as? String,
let imageId = doc.get("imgID") as? String,
let radius = doc.get("radius") as? String {
return PackageInfo(id: doc.documentID, name: title, imageId: imageId, radius: radius)
} else {
return nil
}
}
}
There is no progress reporting within a single read operation, either it's pending or it's completed.
If you want more granular reporting, you can implement pagination yourself so that you know how many items you've already read. If you want to show progress against the total, this means you will also need to track the total count yourself though.
I am creating an app which detect the exercises. i trained the model using create ML. i got 100% result in create ML application. But when i am integrating into the application using Vision framework it's always showing only one exercise. i followed the code exactly from Build an Action Classifier with Create ML for creating ml and requesting VNHumanBodyPoseObservation. Followed this for converting VNHumanBodyPoseObservation to MLMultiArray.
Here is the code what i do:
func didOutput(pixelBuffer: CVPixelBuffer) {
self.extractPoses(pixelBuffer)
}
func extractPoses(_ pixelBuffer: CVPixelBuffer) {
let handler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer)
let request = VNDetectHumanBodyPoseRequest { (request, err) in
if err == nil {
if let observations =
request.results as? [VNRecognizedPointsObservation], observations.count > 0 {
if let prediction = try? self.makePrediction(observations) {
print("\(prediction.label), confidence: \(prediction.confidence)")
}
}
}
}
do {
// Perform the body pose-detection request.
try handler.perform([request])
} catch {
print("Unable to perform the request: \(error).\n")
}
}
func makePrediction(_ observations: [VNRecognizedPointsObservation]) throws -> (label: String, confidence: Double) {
let fitnessClassifier = try PlayerExcercise(configuration: MLModelConfiguration())
let numAvailableFrames = observations.count
let observationsNeeded = 60
var multiArrayBuffer = [MLMultiArray]()
for frameIndex in 0 ..< min(numAvailableFrames, observationsNeeded) {
let pose = observations[frameIndex]
do {
let oneFrameMultiArray = try pose.keypointsMultiArray()
multiArrayBuffer.append(oneFrameMultiArray)
} catch {
continue
}
}
// If poseWindow does not have enough frames (45) yet, we need to pad 0s
if numAvailableFrames < observationsNeeded {
for _ in 0 ..< (observationsNeeded - numAvailableFrames) {
do {
let oneFrameMultiArray = try MLMultiArray(shape: [1, 3, 18], dataType: .double)
try resetMultiArray(oneFrameMultiArray)
multiArrayBuffer.append(oneFrameMultiArray)
} catch {
continue
}
}
}
let modelInput = MLMultiArray(concatenating: [MLMultiArray](multiArrayBuffer), axis: 0, dataType: .float)
//
//
let predictions = try fitnessClassifier.prediction(poses: modelInput)
return (label: predictions.label, confidence: predictions.labelProbabilities[predictions.label]!)
}
func resetMultiArray(_ predictionWindow: MLMultiArray, with value: Double = 0.0) throws {
let pointer = try UnsafeMutableBufferPointer<Double>(predictionWindow)
pointer.initialize(repeating: value)
}
I suspect the issue happening while converting VNRecognizedPointsObservation to MLMultiArray Please help me, i am trying to achieve this so hard. Thanks in advance.
Are you running your app on a simulator? Because I had the same issue that the model predicted wrong results when I ran my image classifier app on a iPhone 12 simulator. But the issue was solved when I tried to run the app on a real device. So maybe there is nothing wrong with your model or code, try running it on a real device and see if you get your intended results.
I trained a CNN classification model using RGB images as input and it produces 1x7 output with probabilities of class labels(7 different classes). I have converted the model from keras .h5 to coreML. I have seen different applications and tried both of them with and without class labels defined. They did not cause any issue while converting. However none of them work in IOS. Both models crash when I call below line:
guard let result = predictionRequest.results as? [VNCoreMLFeatureValueObservation] else {
fatalError("model failed to process image")
}
Output definition of my both models are below. Could you please advice what is wrong with the model output. Do I have to add class labels or not? I am confused how to call the highest probable value. I have added entire classification code too. Please see below. Since I am a beginner in IOS, your help is greatly appreciated. Thanks a lot indeed.
Model output definition in IOS with class labels conversion:
/// Identity as dictionary of strings to doubles
lazy var Identity: [String : Double] = {
[unowned self] in return self.provider.featureValue(for: "Identity")!.dictionaryValue as! [String : Double]
}()
/// classLabel as string value
lazy var classLabel: String = {
[unowned self] in return self.provider.featureValue(for: "classLabel")!.stringValue
}()
Model output definition in IOS without class labels conversion:
init(Identity: MLMultiArray) {
self.provider = try! MLDictionaryFeatureProvider(dictionary: ["Identity" : MLFeatureValue(multiArray: Identity)])
}
Classification Code:
class ColorStyleVisionManager: NSObject {
static let shared = ColorStyleVisionManager()
static let MODEL = hair_color_class_labels().model
var colorStyle = String()
var hairColorFlag: Int = 0
private lazy var predictionRequest: VNCoreMLRequest = {
do{
let model = try VNCoreMLModel(for: ColorStyleVisionManager.MODEL)
let request = VNCoreMLRequest(model: model)
request.imageCropAndScaleOption = VNImageCropAndScaleOption.centerCrop
return request
} catch {
fatalError("can't load Vision ML Model")
}
}()
func predict(image:CIImage) -> String {
guard let result = predictionRequest.results as? [VNCoreMLFeatureValueObservation] else {
fatalError("model failed to process image")
}
let firstResult = result.first
if firstResult?.featureName == "0" {
colorStyle = "Plain Coloring"
hairColorFlag = 1
}
else if firstResult?.featureName == "1" {
colorStyle = "Ombre"
hairColorFlag = 2
}
else if firstResult?.featureName == "2" {
colorStyle = "Sombre"
hairColorFlag = 2
}
else if firstResult?.featureName == "3" {
colorStyle = "HighLight"
hairColorFlag = 3
}
else if firstResult?.featureName == "4" {
colorStyle = "LowLight"
hairColorFlag = 3
}
else if firstResult?.featureName == "5" {
colorStyle = "Color Melt"
hairColorFlag = 5
}
else if firstResult?.featureName == "6" {
colorStyle = "Dip Dye"
hairColorFlag = 4
}
else {}
let handler = VNImageRequestHandler(ciImage: image)
do {
try handler.perform([predictionRequest])
} catch {
print("error handler")
}
return colorStyle
}
}
I have found out two different problems in my code. In order to ensure that my model correctly converted to mlmodel, I created a new classification mlmodel by using Apple's CreateML tool. By the way it is fantastic even though the accuracy seems lower than my original model. I compared the output and input types of the model and seems my mlmodel is correct too. Then I used this model and gave it another try. It crashed again. I wasn't so sure what prediction result I have to expect whether "VNClassificationObservation" or "VNCoreMLFeatureValueObservation". I changed to classificationobservation. It crashed again. Then I realized that my handler definition was below the crash line and I moved it to upper portion. Then woola. It worked. I double checked by changing the FeatureValueObservation and it crashed again. So two problems are solved. Please see the correct code below.
I strongly recommend to use CreateML tool to confirm your model conversion work fine for debugging purposes. It is just a few minutes job.
class ColorStyleVisionManager: NSObject {
static let shared = ColorStyleVisionManager()
static let MODEL = hair_color_class_labels().model
var colorStyle = String()
var hairColorFlag: Int = 0
private lazy var predictionRequest: VNCoreMLRequest = {
do{
let model = try VNCoreMLModel(for: ColorStyleVisionManager.MODEL)
let request = VNCoreMLRequest(model: model)
request.imageCropAndScaleOption = VNImageCropAndScaleOption.centerCrop
return request
} catch {
fatalError("can't load Vision ML Model")
}
}()
func predict(image:CIImage) -> String {
let handler = VNImageRequestHandler(ciImage: image)
do {
try handler.perform([predictionRequest])
} catch {
print("error handler")
}
guard let result = predictionRequest.results as? [VNClassificationObservation] else {
fatalError("error to process request")
}
let firstResult = result.first
print(firstResult!)
On the 3rd line in the function below I get the following error:
Unable to infer closure type in the current context
How do I fix this?
func fetchAllUsersImages() {
print("inside func")
self.ref.child("Posts").child(self.userID).child(self.postNum).observe(.childAdded, with: { snapshot in //error here
var images: [URL] = []
if let snapShotValue = snapshot.value as? [String: String] {
for (_, value) in snapShotValue {
if let imageURL = URL(string: value) {
print(imageURL, "image url here")
let imageAsData = try Data(contentsOf: imageURL)
let image = UIImage(data: imageAsData)
let ImageObject = Image()
ImageObject.image = image
self.arrayOfImgObj.append(ImageObject)
self.tableView.reloadData()
}
}
}
})
}
The reason why it is not inferring the closure type is because the try statement is not handled. This means that the closure expected to "catch" the error, but in your case, you forgot the do-try-catch rule.
Therefore you can try the following answer which will catch your errors:
do {
let imageAsData = try Data(contentsOf: imageURL)
let image = UIImage(data: imageAsData)
let ImageObject = Image()
ImageObject.image = image
self.arrayOfImgObj.append(ImageObject)
} catch {
print("imageURL was not able to be converted into data") // Assert or add an alert
}
You can then assert an error (for testing), or what I would personally do, is set up an alert.
This way the app wouldn't crash, but instead, notify the user. I find this very helpful when on the go and my device isn't plugged in - so I can see the error messages instead of a blank crash with no idea what happened.
This error can also happen if you have a non related compilation error in your closure body. For example, you may be trying to compare two or more non-boolean types.
extension Array where Element == Resistance {
init(_ points: [Point]) {
let peaks = points.beforeAndAfter { (before, current, after) -> Bool in
before < current && current > after
}
self = []
}
}
will produce Unable to infer closure type in the current context.
The correct code:
extension Array where Element == Resistance {
init(_ points: [Point]) {
let peaks = points.beforeAndAfter { (before, current, after) -> Bool in
before.value < current.value && current.value > after.value
}
self = []
}
}
In addition to ScottyBlades answer, I'd like to add two data points to the "experience". It looks like referencing a non-existent property using self inside the block is not handled nicely by the compiler.
Nice error inside the block:
// Setting a handler for an NWListener instance:
self.nwListener?.newConnectionHandler = { (_ connection: NWConnection) -> Void in
// Results in "Cannot find 'nonExistentProperty' in scope"
// in the line below:
guard let delegate = nonExistentProperty else { return }
}
Weird "Type of expression is ambiguous without more context" error: (note the new self in front of nonExistentProperty)
// Setting a handler for an NWListener instance:
// Results in "Type of expression is ambiguous without more context"
// at the equals sign below:
self.nwListener?.newConnectionHandler = { (_ connection: NWConnection) -> Void in
guard let delegate = self.nonExistentProperty else { return }
}
I'm looking through the Apple's Vision API documentation and I see a couple of classes that relate to text detection in UIImages:
1) class VNDetectTextRectanglesRequest
2) class VNTextObservation
It looks like they can detect characters, but I don't see a means to do anything with the characters. Once you've got characters detected, how would you go about turning them into something that can be interpreted by NSLinguisticTagger?
Here's a post that is a brief overview of Vision.
Thank you for reading.
This is how to do it ...
//
// ViewController.swift
//
import UIKit
import Vision
import CoreML
class ViewController: UIViewController {
//HOLDS OUR INPUT
var inputImage:CIImage?
//RESULT FROM OVERALL RECOGNITION
var recognizedWords:[String] = [String]()
//RESULT FROM RECOGNITION
var recognizedRegion:String = String()
//OCR-REQUEST
lazy var ocrRequest: VNCoreMLRequest = {
do {
//THIS MODEL IS TRAINED BY ME FOR FONT "Inconsolata" (Numbers 0...9 and UpperCase Characters A..Z)
let model = try VNCoreMLModel(for:OCR().model)
return VNCoreMLRequest(model: model, completionHandler: self.handleClassification)
} catch {
fatalError("cannot load model")
}
}()
//OCR-HANDLER
func handleClassification(request: VNRequest, error: Error?)
{
guard let observations = request.results as? [VNClassificationObservation]
else {fatalError("unexpected result") }
guard let best = observations.first
else { fatalError("cant get best result")}
self.recognizedRegion = self.recognizedRegion.appending(best.identifier)
}
//TEXT-DETECTION-REQUEST
lazy var textDetectionRequest: VNDetectTextRectanglesRequest = {
return VNDetectTextRectanglesRequest(completionHandler: self.handleDetection)
}()
//TEXT-DETECTION-HANDLER
func handleDetection(request:VNRequest, error: Error?)
{
guard let observations = request.results as? [VNTextObservation]
else {fatalError("unexpected result") }
// EMPTY THE RESULTS
self.recognizedWords = [String]()
//NEEDED BECAUSE OF DIFFERENT SCALES
let transform = CGAffineTransform.identity.scaledBy(x: (self.inputImage?.extent.size.width)!, y: (self.inputImage?.extent.size.height)!)
//A REGION IS LIKE A "WORD"
for region:VNTextObservation in observations
{
guard let boxesIn = region.characterBoxes else {
continue
}
//EMPTY THE RESULT FOR REGION
self.recognizedRegion = ""
//A "BOX" IS THE POSITION IN THE ORIGINAL IMAGE (SCALED FROM 0... 1.0)
for box in boxesIn
{
//SCALE THE BOUNDING BOX TO PIXELS
let realBoundingBox = box.boundingBox.applying(transform)
//TO BE SURE
guard (inputImage?.extent.contains(realBoundingBox))!
else { print("invalid detected rectangle"); return}
//SCALE THE POINTS TO PIXELS
let topleft = box.topLeft.applying(transform)
let topright = box.topRight.applying(transform)
let bottomleft = box.bottomLeft.applying(transform)
let bottomright = box.bottomRight.applying(transform)
//LET'S CROP AND RECTIFY
let charImage = inputImage?
.cropped(to: realBoundingBox)
.applyingFilter("CIPerspectiveCorrection", parameters: [
"inputTopLeft" : CIVector(cgPoint: topleft),
"inputTopRight" : CIVector(cgPoint: topright),
"inputBottomLeft" : CIVector(cgPoint: bottomleft),
"inputBottomRight" : CIVector(cgPoint: bottomright)
])
//PREPARE THE HANDLER
let handler = VNImageRequestHandler(ciImage: charImage!, options: [:])
//SOME OPTIONS (TO PLAY WITH..)
self.ocrRequest.imageCropAndScaleOption = VNImageCropAndScaleOption.scaleFill
//FEED THE CHAR-IMAGE TO OUR OCR-REQUEST - NO NEED TO SCALE IT - VISION WILL DO IT FOR US !!
do {
try handler.perform([self.ocrRequest])
} catch { print("Error")}
}
//APPEND RECOGNIZED CHARS FOR THAT REGION
self.recognizedWords.append(recognizedRegion)
}
//THATS WHAT WE WANT - PRINT WORDS TO CONSOLE
DispatchQueue.main.async {
self.PrintWords(words: self.recognizedWords)
}
}
func PrintWords(words:[String])
{
// VOILA'
print(recognizedWords)
}
func doOCR(ciImage:CIImage)
{
//PREPARE THE HANDLER
let handler = VNImageRequestHandler(ciImage: ciImage, options:[:])
//WE NEED A BOX FOR EACH DETECTED CHARACTER
self.textDetectionRequest.reportCharacterBoxes = true
self.textDetectionRequest.preferBackgroundProcessing = false
//FEED IT TO THE QUEUE FOR TEXT-DETECTION
DispatchQueue.global(qos: .userInteractive).async {
do {
try handler.perform([self.textDetectionRequest])
} catch {
print ("Error")
}
}
}
override func viewDidLoad() {
super.viewDidLoad()
// Do any additional setup after loading the view, typically from a nib.
//LETS LOAD AN IMAGE FROM RESOURCE
let loadedImage:UIImage = UIImage(named: "Sample1.png")! //TRY Sample2, Sample3 too
//WE NEED A CIIMAGE - NOT NEEDED TO SCALE
inputImage = CIImage(image:loadedImage)!
//LET'S DO IT
self.doOCR(ciImage: inputImage!)
}
override func didReceiveMemoryWarning() {
super.didReceiveMemoryWarning()
// Dispose of any resources that can be recreated.
}
}
You'll find the complete project here included is the trained model !
SwiftOCR
I just got SwiftOCR to work with small sets of text.
https://github.com/garnele007/SwiftOCR
uses
https://github.com/Swift-AI/Swift-AI
which uses NeuralNet-MNIST model for text recognition.
TODO : VNTextObservation > SwiftOCR
Will post example of it using VNTextObservation once I have it one connected to the other.
OpenCV + Tesseract OCR
I tried to use OpenCV + Tesseract but got compile errors then found SwiftOCR.
SEE ALSO : Google Vision iOS
Note Google Vision Text Recognition - Android sdk has text detection but also has iOS cocoapod. So keep an eye on it as should add text recognition to the iOS eventually.
https://developers.google.com/vision/text-overview
//Correction: just tried it but only Android version of the sdk supports text detection.
https://developers.google.com/vision/text-overview
If you subscribe to releases:
https://libraries.io/cocoapods/GoogleMobileVision
Click SUBSCRIBE TO RELEASES
you can see when TextDetection is added to the iOS part of the Cocoapod
Apple finally updated Vision to do OCR. Open a playground and dump a couple of test images in the Resources folder. In my case, I called them "demoDocument.jpg" and "demoLicensePlate.jpg".
The new class is called VNRecognizeTextRequest. Dump this in a playground and give it a whirl:
import Vision
enum DemoImage: String {
case document = "demoDocument"
case licensePlate = "demoLicensePlate"
}
class OCRReader {
func performOCR(on url: URL?, recognitionLevel: VNRequestTextRecognitionLevel) {
guard let url = url else { return }
let requestHandler = VNImageRequestHandler(url: url, options: [:])
let request = VNRecognizeTextRequest { (request, error) in
if let error = error {
print(error)
return
}
guard let observations = request.results as? [VNRecognizedTextObservation] else { return }
for currentObservation in observations {
let topCandidate = currentObservation.topCandidates(1)
if let recognizedText = topCandidate.first {
print(recognizedText.string)
}
}
}
request.recognitionLevel = recognitionLevel
try? requestHandler.perform([request])
}
}
func url(for image: DemoImage) -> URL? {
return Bundle.main.url(forResource: image.rawValue, withExtension: "jpg")
}
let ocrReader = OCRReader()
ocrReader.performOCR(on: url(for: .document), recognitionLevel: .fast)
There's an in-depth discussion of this from WWDC19
Adding my own progress on this, if anyone have a better solution:
I've successfully drawn the region box and character boxes on screen. The vision API of Apple is actually very performant. You have to transform each frame of your video to an image and feed it to the recogniser. It's much more accurate than feeding directly the pixel buffer from the camera.
if #available(iOS 11.0, *) {
guard let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else {return}
var requestOptions:[VNImageOption : Any] = [:]
if let camData = CMGetAttachment(sampleBuffer, kCMSampleBufferAttachmentKey_CameraIntrinsicMatrix, nil) {
requestOptions = [.cameraIntrinsics:camData]
}
let imageRequestHandler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer,
orientation: 6,
options: requestOptions)
let request = VNDetectTextRectanglesRequest(completionHandler: { (request, _) in
guard let observations = request.results else {print("no result"); return}
let result = observations.map({$0 as? VNTextObservation})
DispatchQueue.main.async {
self.previewLayer.sublayers?.removeSubrange(1...)
for region in result {
guard let rg = region else {continue}
self.drawRegionBox(box: rg)
if let boxes = region?.characterBoxes {
for characterBox in boxes {
self.drawTextBox(box: characterBox)
}
}
}
}
})
request.reportCharacterBoxes = true
try? imageRequestHandler.perform([request])
}
}
Now I'm trying to actually reconize the text. Apple doesn't provide any built in OCR model. And I want to use CoreML to do that, so I'm trying to convert a Tesseract trained data model to CoreML.
You can find Tesseract models here: https://github.com/tesseract-ocr/tessdata and I think the next step is to write a coremltools converter that support those type of input and output a .coreML file.
Or, you can link to TesseractiOS directly and try to feed it with your region boxes and character boxes you get from the Vision API.
Thanks to a GitHub user, you can test an example: https://gist.github.com/Koze/e59fa3098388265e578dee6b3ce89dd8
- (void)detectWithImageURL:(NSURL *)URL
{
VNImageRequestHandler *handler = [[VNImageRequestHandler alloc] initWithURL:URL options:#{}];
VNDetectTextRectanglesRequest *request = [[VNDetectTextRectanglesRequest alloc] initWithCompletionHandler:^(VNRequest * _Nonnull request, NSError * _Nullable error) {
if (error) {
NSLog(#"%#", error);
}
else {
for (VNTextObservation *textObservation in request.results) {
// NSLog(#"%#", textObservation);
// NSLog(#"%#", textObservation.characterBoxes);
NSLog(#"%#", NSStringFromCGRect(textObservation.boundingBox));
for (VNRectangleObservation *rectangleObservation in textObservation.characterBoxes) {
NSLog(#" |-%#", NSStringFromCGRect(rectangleObservation.boundingBox));
}
}
}
}];
request.reportCharacterBoxes = YES;
NSError *error;
[handler performRequests:#[request] error:&error];
if (error) {
NSLog(#"%#", error);
}
}
The thing is, the result is an array of bounding boxes for each detected character. From what I gathered from Vision's session, I think you are supposed to use CoreML to detect the actual chars.
Recommended WWDC 2017 talk: Vision Framework: Building on Core ML (haven't finished watching it either), have a look at 25:50 for a similar example called MNISTVision
Here's another nifty app demonstrating the use of Keras (Tensorflow) for the training of a MNIST model for handwriting recognition using CoreML: Github
I'm using Google's Tesseract OCR engine to convert the images into actual strings. You'll have to add it to your Xcode project using cocoapods. Although Tesseract will perform OCR even if you simply feed the image containing texts to it, the way to make it perform better/faster is to use the detected text rectangles to feed pieces of the image that actually contain text, which is where Apple's Vision Framework comes in handy.
Here's a link to the engine:
Tesseract OCR
And here's a link to the current stage of my project that has text detection + OCR already implemented:
Out Loud - Camera to Speech
Hope these can be of some use. Good luck!
For those still looking for a solution I wrote a quick library to do this. It uses both the Vision API and Tesseract and can be used to achieve the task the question describes with one single method:
func sliceaAndOCR(image: UIImage, charWhitelist: String, charBlackList: String = "", completion: #escaping ((_: String, _: UIImage) -> Void))
This method will look for text in your image, return the string found and a slice of the original image showing where the text was found
Firebase ML Kit does it for iOS (and Android) with their on-device Vision API and it outperforms Tesseract and SwiftOCR.