vision-framework

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.

Aspect	Modern (iOS 18+)	Legacy
Pattern	let result = try await request.perform(on: image)	VNImageRequestHandler + completion handler
Request types	Swift types — structs and classes (RecognizeTextRequest, DetectFaceRectanglesRequest)	ObjC classes (VNRecognizeTextRequest, VNDetectFaceRectanglesRequest)
Concurrency	Native async/await	Completion handlers or synchronous perform
Observations	Typed return values	Cast results from [Any]
Availability	iOS 18+ / macOS 15+	iOS 11+

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:

Request	Purpose
ClassifyImageRequest	Classify scene content (outdoor, food, animal, etc.)
GenerateAttentionBasedSaliencyImageRequest	Heat map of where viewers focus attention
GenerateObjectnessBasedSaliencyImageRequest	Heat map of object-like regions
GenerateForegroundInstanceMaskRequest	Foreground object segmentation (not person-specific)
DetectRectanglesRequest	Detect rectangular shapes (documents, cards, screens)
DetectHorizonRequest	Detect horizon angle for auto-leveling photos
DetectHumanBodyPoseRequest	Detect body joints (shoulders, elbows, knees)
DetectHumanBodyPose3DRequest	3D human body pose estimation
DetectHumanHandPoseRequest	Detect hand joints and finger positions
DetectAnimalBodyPoseRequest	Detect animal body joint positions
DetectFaceCaptureQualityRequest	Face capture quality scoring (0–1) for photo selection
TrackRectangleRequest	Track rectangular objects across video frames
TrackOpticalFlowRequest	Optical flow between video frames
DetectTrajectoriesRequest	Detect object trajectories in video

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.

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