The document discusses setting up real-time text recognition from video using Vision and CoreML on iOS. It involves initializing an AVCaptureSession to capture video from the device camera, setting up an AVCaptureVideoDataOutput to receive sample buffers, passing sample buffers to a VNRecognizeTextRequest to perform text recognition using Vision, and processing the results.

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29. //
let captureSession = AVCaptureSession()
//
captureSession.sessionPreset = .hd1280x720

30. //
let captureSession = AVCaptureSession()
//
captureSession.sessionPreset = .hd1280x720

31. //
let captureSession = AVCaptureSession()
//
captureSession.sessionPreset = .hd1280x720

32. //
let camera = AVCaptureDevice.default(
.builtInWideAngleCamera,
for: .video,
position: .back)!
//
let deviceInput
= try! AVCaptureDeviceInput(device: camera)
captureSession.addInput(deviceInput)

33. //
let camera = AVCaptureDevice.default(
.builtInWideAngleCamera,
for: .video,
position: .back)!
//
let deviceInput
= try! AVCaptureDeviceInput(device: camera)
captureSession.addInput(deviceInput)

34. //
let videoOutput =
AVCaptureVideoDataOutput()
//
let videoQueue =
DispatchQueue(label: "VideoQueue")
//
videoOutput.setSampleBufferDelegate(self,
queue: videoQueue)
captureSession.addOutput(videoDataOutput)

35. //
let videoOutput =
AVCaptureVideoDataOutput()
//
let videoQueue =
DispatchQueue(label: "VideoQueue")
//
videoOutput.setSampleBufferDelegate(self,
queue: videoQueue)
captureSession.addOutput(videoDataOutput)

36. //
try! camera.lockForConfiguration()
camera.videoZoomFactor = 1.2
camera.autoFocusRangeRestriction = .near
camera.unlockForConfiguration()

37. //
let previewLayer
= AVCaptureVideoPreviewLayer(
session: captureSession)
previewLayer.videoGravity
= .resizeAspectFill
// view: View
previewLayer.frame = view.bounds
view.layer.addSublayer(previewLayer)

38. //
var request: VNRecognizeTextRequest!
request = VNRecognizeTextRequest { _, _ in
…
}
//
request.usesLanguageCorrection = false
request.recognitionLevel = .accurate
request.regionOfInterest
= CGRect(x: 0, y: 0.8,
width: 1, height: 0.2)

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W=1
H=1
0.2
(0,0)
(0,0.8)

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43. //
captureSession.startRunning()

44. //
captureSession.startRunning()
//AVCaptureVideoDataOutputSampleBufferDelegate
func captureOutput(_ output: AVCaptureOutput,
didOutput sampleBuffer: CMSampleBuffer,
from connection: AVCaptureConnection)

45. //
captureSession.startRunning()
//AVCaptureVideoDataOutputSampleBufferDelegate
func captureOutput(_ output: AVCaptureOutput,
didOutput sampleBuffer: CMSampleBuffer,
from connection: AVCaptureConnection)

46. // Vision
let pixelBuffer =
CMSampleBufferGetImageBuffer(sampleBuffer)!
//
let requestHandler =
VNImageRequestHandler(
cvPixelBuffer: pixelBuffer,
orientation: .right, options: [:])
//
try! requestHandler.perform([request])

47. request = VNRecognizeTextRequest
{ request, error in
//
}
//
try! requestHandler.perform([request])

48. request = VNRecognizeTextRequest
{ request, error in
//
}
//
try! requestHandler.perform([request])

49. let results = request.results
as! [VNRecognizedTextObservation]
results.forEach { result in
guard let candidate =
result.topCandidates(1).first
else { return }
let text = candidate.string
…
}

50. let results = request.results
as! [VNRecognizedTextObservation]
results.forEach { result in
guard let candidate =
result.topCandidates(1).first
else { return }
let text = candidate.string
…
}

51. let results = request.results
as! [VNRecognizedTextObservation]
results.forEach { result in
guard let candidate =
result.topCandidates(1).first
else { return }
let text = candidate.string
…
}

52. let pattern =
#"w{4}[ ]w{4}[ ]w{4}[ ]w{4}"#
guard let range = text.range(of: pattern,
options: .regularExpression)
else { return }
//
// o => 0, l => 1, s => 5, B => 8, etc.
…
//
…

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