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.