vision-framework

name

vision-framework

description

Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 18+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and CoreMLRequest/VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.

Vision Framework

Detect text, faces, barcodes, objects, and body poses in images and video using on-device computer vision. Prefer the modern iOS 18+ request APIs and load the legacy reference only when the deployment target requires it.

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: Swift-native request types plus try await request.perform(on:). Keep VN*, VNImageRequestHandler, VNSequenceRequestHandler, completion handlers, and legacy CGRect helpers inside explicit legacy fallback sections or files.

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 such as GeneratePersonSegmentationRequest, TrackObjectRequest, TrackRectangleRequest, and DetectTrajectoriesRequest are final classes.

Request Pattern (Modern API)

All modern Vision requests follow the same pattern: create a request, 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)

For pre-iOS 18 targets, use the corresponding VNRequest with VNImageRequestHandler or VNSequenceRequestHandler. Load references/vision-requests.md for complete legacy request and handler patterns.

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   // NormalizedRect
}

Legacy: VNRecognizeTextRequest

The legacy request uses string language identifiers and the handler pattern in the reference; both generations support accurate and fast 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   // NormalizedRect
    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.points
    let nose = landmarks?.nose.points
}

Coordinate System

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

import Vision

func imageRectForDisplay(_ rect: NormalizedRect, imageSize: CGSize) -> CGRect {
    rect.toImageCoordinates(imageSize, origin: .upperLeft)
}

Barcode Detection

Detect 1D and 2D barcodes including QR codes.

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

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             // NormalizedRect
}

Type annotate local values first, then assign request properties separately.

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. Currently, Vision returns one document observation for each image.

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 PixelBufferObservation with a pixelBuffer property
let maskBuffer = mask.pixelBuffer
// Apply mask using Core Image: CIFilter.blendWithMask()

Legacy: VNGeneratePersonSegmentationRequest

For older targets, VNGeneratePersonSegmentationRequest exposes its mask through the first pixel-buffer observation; use the reference's handler and mask-composition recipe.

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(for: 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.

// Initialize with a detected object's bounding box
let initialObservation = DetectedObjectObservation(boundingBox: detectedBox)
let request = TrackObjectRequest(detectedObject: initialObservation)

for pixelBuffer in framePixelBuffers {
    let results = try await request.perform(on: pixelBuffer)
    if let tracked = results.first {
        let updatedBounds = tracked.boundingBox  // NormalizedRect
    }
}

Modern TrackObjectRequest has no trackingLevel or qualityLevel.

Legacy: VNTrackObjectRequest

For older targets, use VNTrackObjectRequest with one retained VNSequenceRequestHandler and feed each result back as the next input observation. The reference contains the complete loop.

Other Request Types

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

Request	Purpose
ClassifyImageRequest	Classify scene content (outdoor, food, animal, etc.)
GenerateAttentionBasedSaliencyImageRequest	Single SaliencyImageObservation for where viewers focus attention
GenerateObjectnessBasedSaliencyImageRequest	Single SaliencyImageObservation for 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.

Vision runs already-prepared models with CoreMLRequest or VNCoreMLRequest; hand conversion, profiling, packaging, and lifecycle decisions to coreml.

import CoreML
import Vision

// Modern API (iOS 18+): CoreMLRequest takes a CoreMLModelContainer.
let model = try MLModel(contentsOf: modelURL)
let container = try CoreMLModelContainer(model: model, featureProvider: nil)
let request = CoreMLRequest(model: container)
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
}

CoreMLModelContainer is the public iOS 18+ Vision container for CoreMLRequest: load an MLModel, wrap it with CoreMLModelContainer(model:featureProvider:), then pass that container to CoreMLRequest(model:). State result mapping when reviewing Core ML through Vision: classifiers produce ClassificationObservation, image outputs produce PixelBufferObservation, and general predictors produce CoreMLFeatureValueObservation.

// 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])

VisionKit: DataScannerViewController

DataScannerViewController provides a live camera scanner for text and barcodes; see references/visionkit-scanner.md. VisionKit uses VNBarcodeSymbology; modern DetectBarcodesRequest uses BarcodeSymbology.

Quick Start

import AVFoundation
import Vision
import VisionKit

@MainActor
func presentScanner() async {
    // Add NSCameraUsageDescription before requesting camera access.
    guard await AVCaptureDevice.requestAccess(for: .video) else { return }
    guard DataScannerViewController.isSupported,
          DataScannerViewController.isAvailable else { return }

    let scannerSymbologies: [VNBarcodeSymbology] = [.qr, .ean13]
    let scanner = DataScannerViewController(
        recognizedDataTypes: [
            .text(languages: ["en"]),
            .barcode(symbologies: scannerSymbologies)
        ],
        qualityLevel: .balanced,
        recognizesMultipleItems: true,
        isHighFrameRateTrackingEnabled: true,
        isHighlightingEnabled: true
    )
    scanner.delegate = self
    present(scanner, animated: true) {
        // Start scanning after presentation, on the main actor.
        try? scanner.startScanning()
    }
}

SwiftUI Integration

Wrap DataScannerViewController in UIViewControllerRepresentable and start in updateUIViewController with Task { @MainActor in try? controller.startScanning() }; see references/visionkit-scanner.md.

Common Mistakes

DON'T: Use the legacy VNImageRequestHandler API for new iOS 18+ projects. DO: Use modern Swift-native 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 NormalizedRect.toImageCoordinates(_:origin:) for modern observations, or VNImageRectForNormalizedRect(_:_:_:) for legacy CGRect observations. 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: Treat every Vision observation as having the same properties. DO: Check each observation type for its bounding box, confidence, payload, mask, or angle fields before writing shared helpers. Why: Modern Vision returns strongly typed observations, and result shapes vary by request.

DON'T: Recreate stateful tracking requests for each video frame. DO: Keep the same modern TrackObjectRequest instance, or use VNSequenceRequestHandler with legacy tracking requests. Why: Tracking relies on temporal context across frames.

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
• VisionKit camera access requested before presentation and scanning started after presentation
• Person segmentation quality level appropriate for use case
• Stateful tracking request or VNSequenceRequestHandler preserved across video frames
• 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 | CoreMLRequest | CoreMLModelContainer