您可以使用 ML Kit 辨識圖片或影片中的文字，例如路標上的文字。這項功能的主要特性如下：
立即試用
事前準備
- 在 Podfile 中加入下列 ML Kit Pod：
# To recognize Latin script pod 'GoogleMLKit/TextRecognition', '15.5.0' # To recognize Chinese script pod 'GoogleMLKit/TextRecognitionChinese', '15.5.0' # To recognize Devanagari script pod 'GoogleMLKit/TextRecognitionDevanagari', '15.5.0' # To recognize Japanese script pod 'GoogleMLKit/TextRecognitionJapanese', '15.5.0' # To recognize Korean script pod 'GoogleMLKit/TextRecognitionKorean', '15.5.0'
- 安裝或更新專案的 Pod 後，請使用
.xcworkspace開啟 Xcode 專案。Xcode 12.4 以上版本支援 ML Kit。
1. 建立「TextRecognizer」的執行個體
請呼叫 +textRecognizer(options:)，建立 TextRecognizer 的例項，並傳遞與您在上述宣告為依附元件的 SDK 相關的選項：
Swift
// When using Latin script recognition SDK let latinOptions = TextRecognizerOptions() let latinTextRecognizer = TextRecognizer.textRecognizer(options:options) // When using Chinese script recognition SDK let chineseOptions = ChineseTextRecognizerOptions() let chineseTextRecognizer = TextRecognizer.textRecognizer(options:options) // When using Devanagari script recognition SDK let devanagariOptions = DevanagariTextRecognizerOptions() let devanagariTextRecognizer = TextRecognizer.textRecognizer(options:options) // When using Japanese script recognition SDK let japaneseOptions = JapaneseTextRecognizerOptions() let japaneseTextRecognizer = TextRecognizer.textRecognizer(options:options) // When using Korean script recognition SDK let koreanOptions = KoreanTextRecognizerOptions() let koreanTextRecognizer = TextRecognizer.textRecognizer(options:options)
Objective-C
// When using Latin script recognition SDK MLKTextRecognizerOptions *latinOptions = [[MLKTextRecognizerOptions alloc] init]; MLKTextRecognizer *latinTextRecognizer = [MLKTextRecognizer textRecognizerWithOptions:options]; // When using Chinese script recognition SDK MLKChineseTextRecognizerOptions *chineseOptions = [[MLKChineseTextRecognizerOptions alloc] init]; MLKTextRecognizer *chineseTextRecognizer = [MLKTextRecognizer textRecognizerWithOptions:options]; // When using Devanagari script recognition SDK MLKDevanagariTextRecognizerOptions *devanagariOptions = [[MLKDevanagariTextRecognizerOptions alloc] init]; MLKTextRecognizer *devanagariTextRecognizer = [MLKTextRecognizer textRecognizerWithOptions:options]; // When using Japanese script recognition SDK MLKJapaneseTextRecognizerOptions *japaneseOptions = [[MLKJapaneseTextRecognizerOptions alloc] init]; MLKTextRecognizer *japaneseTextRecognizer = [MLKTextRecognizer textRecognizerWithOptions:options]; // When using Korean script recognition SDK MLKKoreanTextRecognizerOptions *koreanOptions = [[MLKKoreanTextRecognizerOptions alloc] init]; MLKTextRecognizer *koreanTextRecognizer = [MLKTextRecognizer textRecognizerWithOptions:options];
2. 準備輸入圖片
將圖片以UIImage 或 CMSampleBufferRef 的形式傳遞至 TextRecognizer 的 process(_:completion:) 方法：使用 UIImage 或 CMSampleBuffer 建立 VisionImage 物件。
如果您使用 UIImage，請按照下列步驟操作：
- 使用
UIImage建立VisionImage物件。請務必指定正確的.orientation。Swift
let image = VisionImage(image: UIImage) visionImage.orientation = image.imageOrientation
Objective-C
MLKVisionImage *visionImage = [[MLKVisionImage alloc] initWithImage:image]; visionImage.orientation = image.imageOrientation;
如果您使用 CMSampleBuffer，請按照下列步驟操作：
-
指定
CMSampleBuffer。如何取得圖片方向：
Swift
func imageOrientation( deviceOrientation: UIDeviceOrientation, cameraPosition: AVCaptureDevice.Position ) -> UIImage.Orientation { switch deviceOrientation { case .portrait: return cameraPosition == .front ? .leftMirrored : .right case .landscapeLeft: return cameraPosition == .front ? .downMirrored : .up case .portraitUpsideDown: return cameraPosition == .front ? .rightMirrored : .left case .landscapeRight: return cameraPosition == .front ? .upMirrored : .down case .faceDown, .faceUp, .unknown: return .up } }Objective-C
- (UIImageOrientation) imageOrientationFromDeviceOrientation:(UIDeviceOrientation)deviceOrientation cameraPosition:(AVCaptureDevicePosition)cameraPosition { switch (deviceOrientation) { case UIDeviceOrientationPortrait: return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationLeftMirrored : UIImageOrientationRight; case UIDeviceOrientationLandscapeLeft: return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationDownMirrored : UIImageOrientationUp; case UIDeviceOrientationPortraitUpsideDown: return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationRightMirrored : UIImageOrientationLeft; case UIDeviceOrientationLandscapeRight: return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationUpMirrored : UIImageOrientationDown; case UIDeviceOrientationUnknown: case UIDeviceOrientationFaceUp: case UIDeviceOrientationFaceDown: return UIImageOrientationUp; } } - 使用
CMSampleBuffer物件和方向建立VisionImage物件：Swift
let image = VisionImage(buffer: sampleBuffer) image.orientation = imageOrientation( deviceOrientation: UIDevice.current.orientation, cameraPosition: cameraPosition)
Objective-C
MLKVisionImage *image = [[MLKVisionImage alloc] initWithBuffer:sampleBuffer]; image.orientation = [self imageOrientationFromDeviceOrientation:UIDevice.currentDevice.orientation cameraPosition:cameraPosition];
3. 處理圖片
接著，將圖片傳遞至 process(_:completion:) 方法：
Swift
textRecognizer.process(visionImage) { result, error in
guard error == nil, let result = result else {
// Error handling
return
}
// Recognized text
}
Objective-C
[textRecognizer processImage:image
completion:^(MLKText *_Nullable result,
NSError *_Nullable error) {
if (error != nil || result == nil) {
// Error handling
return;
}
// Recognized text
}];
4. 從已辨識的文字區塊中擷取文字
如果文字辨識作業成功，系統會傳回 Text 物件。Text 物件包含全文
已辨識在圖片中，且為零或多個 TextBlock
如需儲存大量結構化物件
建議使用 Cloud Bigtable
每個 TextBlock 都代表矩形文字區塊，
包含零個或多個 TextLine 物件。每個 TextLine 物件都包含零個或多個 TextElement 物件，這些物件代表字詞和字詞類型實體，例如日期和數字。
針對每個 TextBlock、TextLine 和
TextElement 物件，您可以在
區域的邊界座標。
例如：
Swift
let resultText = result.text
for block in result.blocks {
let blockText = block.text
let blockLanguages = block.recognizedLanguages
let blockCornerPoints = block.cornerPoints
let blockFrame = block.frame
for line in block.lines {
let lineText = line.text
let lineLanguages = line.recognizedLanguages
let lineCornerPoints = line.cornerPoints
let lineFrame = line.frame
for element in line.elements {
let elementText = element.text
let elementCornerPoints = element.cornerPoints
let elementFrame = element.frame
}
}
}
Objective-C
NSString *resultText = result.text;
for (MLKTextBlock *block in result.blocks) {
NSString *blockText = block.text;
NSArray<MLKTextRecognizedLanguage *> *blockLanguages = block.recognizedLanguages;
NSArray<NSValue *> *blockCornerPoints = block.cornerPoints;
CGRect blockFrame = block.frame;
for (MLKTextLine *line in block.lines) {
NSString *lineText = line.text;
NSArray<MLKTextRecognizedLanguage *> *lineLanguages = line.recognizedLanguages;
NSArray<NSValue *> *lineCornerPoints = line.cornerPoints;
CGRect lineFrame = line.frame;
for (MLKTextElement *element in line.elements) {
NSString *elementText = element.text;
NSArray<NSValue *> *elementCornerPoints = element.cornerPoints;
CGRect elementFrame = element.frame;
}
}
}
輸入圖片規範
-
為了讓 ML Kit 準確辨識文字，輸入圖片必須包含由足夠像素資料代表的文字。理想情況下，每個字元至少應為 16 x 16 像素。一般來說 對字元大於 24x24 像素的特性來說，準確性的優勢在於。
舉例來說，如果要掃描占據整個圖片寬度的名片，使用 640x480 的圖片可能會是個不錯的選擇。如何掃描列印的文件 則建議使用 720x1280 像素的圖片。
-
圖片對焦不佳可能會影響文字辨識準確度。如果您無法取得可接受的結果，請嘗試要求使用者重新拍攝圖片。
-
如果您要在即時應用程式中辨識文字，應考量輸入圖片的整體尺寸。較小 也能加快處理速度如要縮短延遲時間，請確保文字會盡量佔滿 盡可能擷取圖片，並以較低解析度拍攝圖片 (提醒您， 規定)。若需更多資訊，請參閲 提升成效的訣竅。
提升成效的訣竅
- 如要處理影像框，請使用偵測器的
results(in:)同步 API。請從AVCaptureVideoDataOutputSampleBufferDelegate的captureOutput(_, didOutput:from:)函式呼叫這個方法，以便同步取得指定影片影格中的結果。請將AVCaptureVideoDataOutput的alwaysDiscardsLateVideoFrames設為true，以便調節對偵測器的呼叫。如果在偵測器執行期間有新的影片影格可用，系統就會捨棄該影格。 - 如果您使用偵測器的輸出內容，在輸入圖片上疊加圖形，請先從 ML Kit 取得結果，然後在單一步驟中算繪圖片和疊加圖形。這樣一來，您只需為每個已處理的輸入影格轉譯一次顯示介面。請參閱 updatePreviewOverlayViewWithLastFrame 也可以查看一個範例
- 建議您以較低解析度拍攝相片。不過，請務必遵守這個 API 的圖片尺寸規定。
- 為避免可能降低效能，請勿重複執行多個
TextRecognizer執行個體具有不同指令碼選項。