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Vision Framework

Detect text, faces, barcodes, objects, and body poses in images and video using on-device computer vision. Patterns target iOS 26+ with Swift 6.2, backward-compatible where noted.

See references/vision-requests.md for complete code patterns and references/visionkit-scanner.md for DataScannerViewController integration.

Contents

• Two API Generations
• Request Pattern (Modern API)
• Text Recognition (OCR)
• Face Detection
• Barcode Detection
• Document Scanning (iOS 26+)
• Image Segmentation
• Object Tracking
• Other Request Types
• Core ML Integration
• VisionKit: DataScannerViewController
• Common Mistakes
• Review Checklist
• References

Two API Generations

Vision has two distinct API layers. Prefer the modern API for new code.

The modern API uses the ImageProcessingRequest protocol. Each request type has a perform(on:orientation:) method that accepts CGImage, CIImage, CVPixelBuffer, CMSampleBuffer, Data, or URL. Most requests are structs; stateful requests for video tracking (e.g., TrackObjectRequest, TrackRectangleRequest, DetectTrajectoriesRequest) are final classes.

Request Pattern (Modern API)

All modern Vision requests follow the same pattern: create a request struct, call perform(on:), and handle the typed result.

import Vision

func recognizeText(in image: CGImage) async throws -> [String] {
    var request = RecognizeTextRequest()
    request.recognitionLevel = .accurate
    request.recognitionLanguages = [Locale.Language(identifier: "en-US")]

    let observations = try await request.perform(on: image)
    return observations.compactMap { observation in
        observation.topCandidates(1).first?.string
    }
}

Legacy Pattern (Pre-iOS 18)

Use VNImageRequestHandler with completion-based requests when targeting older deployment versions.

import Vision

func recognizeTextLegacy(in image: CGImage) throws -> [String] {
    var recognized: [String] = []
    let request = VNRecognizeTextRequest { request, error in
        guard let observations = request.results as? [VNRecognizedTextObservation] else { return }
        recognized = observations.compactMap { $0.topCandidates(1).first?.string }
    }
    request.recognitionLevel = .accurate

    let handler = VNImageRequestHandler(cgImage: image)
    try handler.perform([request])
    return recognized
}

Text Recognition (OCR)

Modern: RecognizeTextRequest (iOS 18+)

var request = RecognizeTextRequest()
request.recognitionLevel = .accurate       // .fast for real-time
request.recognitionLanguages = [
    Locale.Language(identifier: "en-US"),
    Locale.Language(identifier: "fr-FR"),
]
request.usesLanguageCorrection = true
request.customWords = ["SwiftUI", "Xcode"] // domain-specific terms

let observations = try await request.perform(on: cgImage)
for observation in observations {
    guard let candidate = observation.topCandidates(1).first else { continue }
    let text = candidate.string
    let confidence = candidate.confidence  // 0.0 ... 1.0
    let bounds = observation.boundingBox   // normalized coordinates
}

Legacy: VNRecognizeTextRequest

let request = VNRecognizeTextRequest()
request.recognitionLevel = .accurate
request.recognitionLanguages = ["en-US", "fr-FR"]
request.usesLanguageCorrection = true

Key differences: Modern API uses Locale.Language for languages; legacy uses string identifiers. Both support .accurate (best quality) and .fast (real-time suitable) recognition levels.

Face Detection

Detect face rectangles, landmarks (eyes, nose, mouth), and capture quality.

// Modern API
let faceRequest = DetectFaceRectanglesRequest()
let faces = try await faceRequest.perform(on: cgImage)

for face in faces {
    let boundingBox = face.boundingBox   // normalized CGRect
    let roll = face.roll                 // Measurement<UnitAngle>
    let yaw = face.yaw                  // Measurement<UnitAngle>
}

// Landmarks (eyes, nose, mouth contours)
var landmarkRequest = DetectFaceLandmarksRequest()
let landmarkFaces = try await landmarkRequest.perform(on: cgImage)
for face in landmarkFaces {
    let landmarks = face.landmarks
    let leftEye = landmarks?.leftEye?.normalizedPoints
    let nose = landmarks?.nose?.normalizedPoints
}

Coordinate System

Vision uses a normalized coordinate system with origin at the bottom-left. Convert to UIKit (top-left origin) before display:

func convertToUIKit(_ rect: CGRect, imageHeight: CGFloat) -> CGRect {
    CGRect(
        x: rect.origin.x,
        y: imageHeight - rect.origin.y - rect.height,
        width: rect.width,
        height: rect.height
    )
}

Barcode Detection

Detect 1D and 2D barcodes including QR codes.

var request = DetectBarcodesRequest()
request.symbologies = [.qr, .ean13, .code128, .pdf417]

let barcodes = try await request.perform(on: cgImage)
for barcode in barcodes {
    let payload = barcode.payloadString          // decoded content
    let symbology = barcode.symbology            // .qr, .ean13, etc.
    let bounds = barcode.boundingBox             // normalized rect
}

Common symbologies: .qr, .aztec, .pdf417, .dataMatrix, .ean8, .ean13, .code39, .code128, .upce, .itf14.

Document Scanning (iOS 26+)

RecognizeDocumentsRequest provides structured document reading with layout understanding beyond basic OCR. Returns DocumentObservation objects with a nested Container structure for paragraphs, tables, lists, and barcodes.

var request = RecognizeDocumentsRequest()
let documents = try await request.perform(on: cgImage)

for observation in documents {
    let container = observation.document

    // Full text content
    let fullText = container.text

    // Structured access to paragraphs
    for paragraph in container.paragraphs {
        let paragraphText = paragraph.text
    }

    // Tables and lists
    for table in container.tables { /* structured table data */ }
    for list in container.lists { /* structured list data */ }

    // Embedded barcodes detected within the document
    for barcode in container.barcodes { /* barcode data */ }

    // Document title if detected
    if let title = container.title { print(title) }
}

For simpler document camera scanning, use VisionKit's VNDocumentCameraViewController which provides a full-screen camera UI with auto-capture, perspective correction, and multi-page scanning.

Image Segmentation

Modern: GeneratePersonSegmentationRequest (iOS 18+)

var request = GeneratePersonSegmentationRequest()
request.qualityLevel = .accurate  // .balanced, .fast

let mask = try await request.perform(on: cgImage)
// mask is a PersonSegmentationObservation with a pixelBuffer property
let maskBuffer = mask.pixelBuffer
// Apply mask using Core Image: CIFilter.blendWithMask()

Legacy: VNGeneratePersonSegmentationRequest

let request = VNGeneratePersonSegmentationRequest()
request.qualityLevel = .accurate  // .balanced, .fast
request.outputPixelFormat = kCVPixelFormatType_OneComponent8

let handler = VNImageRequestHandler(cgImage: cgImage)
try handler.perform([request])

guard let mask = request.results?.first?.pixelBuffer else { return }
// Apply mask using Core Image: CIFilter.blendWithMask()

Quality levels:

• .accurate -- best quality, slowest (~1s), full resolution
• .balanced -- good quality, moderate speed (~100ms), 960x540
• .fast -- lowest quality, fastest (~10ms), 256x144, suitable for real-time

Instance Segmentation (iOS 18+)

Separate masks per person for individual effects.

// Modern API (iOS 18+)
let request = GeneratePersonInstanceMaskRequest()
let observation = try await request.perform(on: cgImage)
let indices = observation.allInstances

for index in indices {
    let mask = try observation.generateMask(forInstances: IndexSet(integer: index))
    // mask is a CVPixelBuffer with only this person visible
}



// Legacy API (iOS 17+)
let request = VNGeneratePersonInstanceMaskRequest()
let handler = VNImageRequestHandler(cgImage: cgImage)
try handler.perform([request])

guard let result = request.results?.first else { return }
let indices = result.allInstances
for index in indices {
    let instanceMask = try result.generateMaskedImage(
        ofInstances: IndexSet(integer: index),
        from: handler,
        croppedToInstancesExtent: false
    )
}

See references/vision-requests.md for mask composition and Core Image filter integration patterns.

Object Tracking

Modern: TrackObjectRequest (iOS 18+)

TrackObjectRequest is a stateful request that maintains tracking context across frames. Conforms to both ImageProcessingRequest and StatefulRequest.

// Initialize with a detected object's bounding box
let initialObservation = DetectedObjectObservation(boundingBox: detectedRect)
var request = TrackObjectRequest(observation: initialObservation)
request.trackingLevel = .accurate

// For each video frame:
let results = try await request.perform(on: pixelBuffer)
if let tracked = results.first {
    let updatedBounds = tracked.boundingBox
    let confidence = tracked.confidence
}

Legacy: VNTrackObjectRequest

let trackRequest = VNTrackObjectRequest(detectedObjectObservation: initialObservation)
trackRequest.trackingLevel = .accurate

let sequenceHandler = VNSequenceRequestHandler()
// For each frame:
try sequenceHandler.perform([trackRequest], on: pixelBuffer)
if let result = trackRequest.results?.first {
    let updatedBounds = result.boundingBox
    trackRequest.inputObservation = result
}

Other Request Types

Vision provides additional requests covered in references/vision-requests.md:

All modern request types above are iOS 18+ / macOS 15+.

Core ML Integration

Run custom Core ML models through Vision for automatic image preprocessing (resizing, normalization, color space conversion).

// Modern API (iOS 18+)
let model = try MLModel(contentsOf: modelURL)
let request = CoreMLRequest(model: .init(model))
let results = try await request.perform(on: cgImage)

// Classification model
if let classification = results.first as? ClassificationObservation {
    let label = classification.identifier
    let confidence = classification.confidence
}



// Legacy API
let vnModel = try VNCoreMLModel(for: model)
let request = VNCoreMLRequest(model: vnModel) { request, error in
    guard let results = request.results as? [VNClassificationObservation] else { return }
    let topResult = results.first
}
let handler = VNImageRequestHandler(cgImage: cgImage)
try handler.perform([request])

For model conversion and optimization, see the coreml skill.

VisionKit: DataScannerViewController

DataScannerViewController provides a full-screen live camera scanner for text and barcodes. See references/visionkit-scanner.md for complete patterns.

Quick Start

import VisionKit

// Check availability (requires A12+ chip and camera)
guard DataScannerViewController.isSupported,
      DataScannerViewController.isAvailable else { return }

let scanner = DataScannerViewController(
    recognizedDataTypes: [
        .text(languages: ["en"]),
        .barcode(symbologies: [.qr, .ean13])
    ],
    qualityLevel: .balanced,
    recognizesMultipleItems: true,
    isHighFrameRateTrackingEnabled: true,
    isHighlightingEnabled: true
)
scanner.delegate = self
present(scanner, animated: true) {
    try? scanner.startScanning()
}

SwiftUI Integration

Wrap DataScannerViewController in UIViewControllerRepresentable. See references/visionkit-scanner.md for the full implementation.

Common Mistakes

DON'T: Use the legacy VNImageRequestHandler API for new iOS 18+ projects. DO: Use modern struct-based requests with perform(on:) and async/await. Why: Modern API provides type safety, better Swift concurrency support, and cleaner error handling.

DON'T: Forget to convert normalized coordinates before drawing bounding boxes. DO: Use VNImageRectForNormalizedRect(_:_:_:) or manual conversion from bottom-left origin to UIKit top-left origin. Why: Vision uses normalized coordinates (0...1) with bottom-left origin; UIKit uses points with top-left origin.

DON'T: Run Vision requests on the main thread. DO: Perform requests on a background thread or use async/await from a detached task. Why: Image analysis is CPU/GPU-intensive and blocks the UI if run on the main actor.

DON'T: Use .accurate recognition level for real-time camera feeds. DO: Use .fast for live video, .accurate for still images or offline processing. Why: Accurate recognition is too slow for 30fps video; fast recognition trades quality for speed.

DON'T: Ignore the confidence score on observations. DO: Filter results by confidence threshold (e.g., > 0.5) appropriate for your use case. Why: Low-confidence results are often incorrect and degrade user experience.

DON'T: Create a new VNImageRequestHandler for each frame when tracking objects. DO: Use VNSequenceRequestHandler for video frame sequences. Why: Sequence handler maintains temporal context for tracking; per-frame handlers lose state.

DON'T: Request all barcode symbologies when you only need QR codes. DO: Specify only the symbologies you need in the request. Why: Fewer symbologies means faster detection and fewer false positives.

DON'T: Assume DataScannerViewController is available on all devices. DO: Check both isSupported (hardware) and isAvailable (user permissions) before presenting. Why: Requires A12+ chip; isAvailable also checks camera access authorization.

Review Checklist

• Uses modern Vision API (iOS 18+) unless targeting older deployments
• Vision requests run off the main thread (async/await or background queue)
• Normalized coordinates converted before UI display
• Confidence threshold applied to filter low-quality observations
• Recognition level matches use case (.fast for video, .accurate for stills)
• Language hints set for text recognition when input language is known
• Barcode symbologies limited to only those needed
• DataScannerViewController availability checked before presentation
• Camera usage description (NSCameraUsageDescription) in Info.plist for VisionKit
• Person segmentation quality level appropriate for use case
• VNSequenceRequestHandler used for video frame tracking (not per-frame handler)
• Error handling covers request failures and empty results

References

• Vision request patterns: references/vision-requests.md
• VisionKit scanner integration: references/visionkit-scanner.md
• Apple docs: Vision | VisionKit | RecognizeTextRequest | DataScannerViewController

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