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Choose an image source and the current Vision API
RecognizeTextRequest is Vision’s Swift image-analysis request for recognizing text. It returns recognized-text observations containing text and location information. Choose an input that fits your app: the request can perform on an image URL, image data, a CGImage, CVPixelBuffer, CMSampleBuffer, or CIImage. See Apple’s RecognizeTextRequest documentation for the current API and available configuration.
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Camera capture and OCR are separate responsibilities. Apple’s SwiftUI sample for locating and displaying recognized text demonstrates a camera-photo workflow: it requests camera authorization, captures a photo using AVFoundation, and passes the captured image data to Vision. If your app already has an image—from a file picker, document workflow, or another part of the app—you can send that image to the request without building a camera interface.
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Apple lists Xcode 16 or later and an iPhone running iOS 18 or later as requirements for that sample. Those are sample requirements, not prerequisites for every Vision OCR implementation. Check the compatibility of the API and code against the SDK and deployment target selected for your project.
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Configure recognition for the text you expect
The request exposes settings that shape recognition behavior. Choose them based on the images and text your app handles rather than assuming one configuration is best for every case.
| Setting | What it controls | Practical choice |
|---|---|---|
| Recognition level | Prioritizes fast or accurate recognition. | Use the priority that fits the interaction. Apple’s sample uses the accurate path by default, but the documentation does not provide a general measured speed or accuracy advantage. |
| Recognition languages | Sets the languages to recognize. The ordered list prioritizes its first language. | Specify the languages your app expects when appropriate, and check which are supported for the request settings and OS environment in use. |
| Automatic language detection | Lets the request detect language automatically where supported. | Consider it when the input language is unknown; do not assume every language and recognition configuration is available. |
| Language correction | Enables or disables language correction. | Decide whether correction suits the expected text and how users interact with the recognized result. |
| Custom words | Provides additional words for recognition. | Add domain-specific vocabulary when the app expects terms that may be unfamiliar to general recognition. |
| Minimum text height | Sets a minimum text-height fraction for recognition. | Adjust it when the app has a reason to exclude text below a particular relative size. |
Apple’s current request documentation exposes supported-language discovery and request revision information. Query supported languages for the selected configuration and environment rather than treating a fixed list as universal. Older examples often use VNRecognizeTextRequest; Apple documents that as a separate, older API. Its language identifiers are ISO codes, and the order of its language array sets processing priority. For new Swift code, follow the current RecognizeTextRequest documentation instead of mixing the legacy API’s types or properties into the newer request.
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Perform recognition asynchronously and manage results
Apple’s sample awaits request.perform(on: imageData) and uses Swift concurrency when the image or recognition settings change. A model can own the request configuration and observations, while a SwiftUI view presents the state. Vision does not require a particular state-management architecture; the important points are to run the work asynchronously, replace stale results when the input changes, and make failures visible to the user.
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final class OCRModel {
var observations: [RecognizedTextObservation] = []
var request = RecognizeTextRequest()
func recognize(imageData: Data) async throws {
observations = try await request.perform(on: imageData)
}
}This illustrates the request pattern shown in Apple’s sample; it is not a complete production error-handling implementation. In an app, clear old observations when starting work on a replacement image, handle thrown errors in the calling task, and decide how the UI should represent loading, failure, and an image with no recognized text. Confirm exact compiler and deployment-target compatibility in your project’s selected SDK.
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Display recognized text and optional image overlays
Each returned observation can provide a text candidate and a normalized bounding box. For a text-only interface, present the selected candidate as SwiftUI text and include an empty state when there are no observations. Apple’s sample also enables text selection, which is useful when users need to copy recognized content.
If the interface needs to associate text with its location, draw an overlay from each observation’s bounding box. Convert normalized coordinates to the displayed image’s coordinate space before positioning SwiftUI shapes or labels. Account for the image’s orientation, crop, scaling, and the coordinate origin: a geometrically correct box in image coordinates can still appear misplaced if the displayed image has been cropped or transformed differently. Apple’s sample uses an upper-left origin as an implementation reference.
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Validate with representative images
Apple documents configurable recognition options and provides a sample, but the cited documentation does not establish a general OCR accuracy or latency figure. Fast and accurate are prioritization choices, not quantified guarantees. If performance or recognition quality matters to your product, measure representative inputs on the devices and OS versions your app supports.
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- Include the languages, scripts, and vocabulary your app actually expects.
- Test the text sizes and image quality users are likely to submit.
- Include relevant capture conditions, such as the lighting and camera angles in your use case.
- Compare the configurations you intend to ship, and document the devices, OS versions, and test inputs behind any results you publish.
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