在 iOS 上使用 Firebase ML 識別圖像中的文本

您可以使用 Firebase ML 來識別圖像中的文字。 Firebase ML 既有適合識別影像中文字(例如路標文字)的通用 API,也有針對識別文件文字而最佳化的 API。

在你開始之前

    如果您尚未將 Firebase 新增至您的應用程式中,請按照入門指南中的步驟進行操作。

    使用 Swift Package Manager 安裝和管理 Firebase 相依性。

    1. 在 Xcode 中,開啟應用程式項目,導覽至File > Add Packages
    2. 出現提示時,新增 Firebase Apple 平台 SDK 儲存庫:
    3.   https://github.com/firebase/firebase-ios-sdk.git
    4. 選擇 Firebase ML 庫。
    5. -ObjC標誌新增至目標建置設定的「其他連結器標誌」部分。
    6. 完成後,Xcode 將自動開始在背景解析並下載您的依賴項。

    接下來,執行一些應用程式內設定:

    1. 在您的應用程式中,導入 Firebase:

      迅速

      import FirebaseMLModelDownloader

      Objective-C

      @import FirebaseMLModelDownloader;
  1. 如果您尚未為您的專案啟用基於雲端的 API,請立即執行此操作:

    1. 開啟 Firebase 控制台的Firebase ML API 頁面
    2. 如果您尚未將項目升級到 Blaze 定價計劃,請按一下升​​級來執行此操作。 (只有當您的專案不在 Blaze 計劃中時,系統才會提示您升級。)

      只有 Blaze 等級的項目才能使用基於雲端的 API。

    3. 如果尚未啟用基於雲端的 API,請按一下啟用基於雲端的 API

現在您已準備好開始識別圖像中的文字。

輸入影像指南

  • 為了讓 Firebase ML 準確地識別文本,輸入圖像必須包含由足夠的像素資料表示的文本。理想情況下,對於拉丁文本,每個字元應至少為 16x16 像素。對於中文、日文和韓文文本,每個字元應為 24x24 像素。對於所有語言,大於 24x24 像素的字元通常不會帶來準確性優勢。

    例如,640x480 的影像可能適合掃描佔據影像整個寬度的名片。要掃描列印在 letter 尺寸紙張上的文檔,可能需要 720x1280 像素的圖像。

  • 影像焦點不佳會損害文字辨識的準確性。如果您沒有獲得可接受的結果,請嘗試要求使用者重新捕捉影像。


辨識圖像中的文字

若要識別圖像中的文本,請按如下所述運行文字辨識器。

1. 運行文字辨識器

將影像作為UIImageCMSampleBufferRef傳遞給VisionTextRecognizerprocess(_:completion:)方法:

  1. 透過呼叫cloudTextRecognizer來取得VisionTextRecognizer的實例:

    迅速

    let vision = Vision.vision()
    let textRecognizer = vision.cloudTextRecognizer()
    
    // Or, to provide language hints to assist with language detection:
    // See https://cloud.google.com/vision/docs/languages for supported languages
    let options = VisionCloudTextRecognizerOptions()
    options.languageHints = ["en", "hi"]
    let textRecognizer = vision.cloudTextRecognizer(options: options)
    

    Objective-C

    FIRVision *vision = [FIRVision vision];
    FIRVisionTextRecognizer *textRecognizer = [vision cloudTextRecognizer];
    
    // Or, to provide language hints to assist with language detection:
    // See https://cloud.google.com/vision/docs/languages for supported languages
    FIRVisionCloudTextRecognizerOptions *options =
            [[FIRVisionCloudTextRecognizerOptions alloc] init];
    options.languageHints = @[@"en", @"hi"];
    FIRVisionTextRecognizer *textRecognizer = [vision cloudTextRecognizerWithOptions:options];
    
  2. 為了呼叫 Cloud Vision,映像必須格式化為 base64 編碼的字串。處理UIImage

    迅速

    guard let imageData = uiImage.jpegData(compressionQuality: 1.0) else { return }
    let base64encodedImage = imageData.base64EncodedString()

    Objective-C

    NSData *imageData = UIImageJPEGRepresentation(uiImage, 1.0f);
    NSString *base64encodedImage =
      [imageData base64EncodedStringWithOptions:NSDataBase64Encoding76CharacterLineLength];
  3. 然後,將影像傳遞給process(_:completion:)方法:

    迅速

    textRecognizer.process(visionImage) { result, error in
      guard error == nil, let result = result else {
        // ...
        return
      }
    
      // Recognized text
    }
    

    Objective-C

    [textRecognizer processImage:image
                      completion:^(FIRVisionText *_Nullable result,
                                   NSError *_Nullable error) {
      if (error != nil || result == nil) {
        // ...
        return;
      }
    
      // Recognized text
    }];
    

2. 從識別的文本區塊中提取文本

如果文字辨識操作成功,將會傳回一個VisionText物件。 VisionText物件包含影像中辨識的全文以及零個或多個VisionTextBlock物件。

每個VisionTextBlock代表一個矩形文字區塊,其中包含零個或多個VisionTextLine物件。每個VisionTextLine物件包含零個或多個VisionTextElement對象,這些物件表示單字和類似單字的實體(日期、數字等)。

對於每個VisionTextBlockVisionTextLineVisionTextElement對象,您可以獲得該區域中識別的文字以及該區域的邊界座標。

例如:

迅速

let resultText = result.text
for block in result.blocks {
    let blockText = block.text
    let blockConfidence = block.confidence
    let blockLanguages = block.recognizedLanguages
    let blockCornerPoints = block.cornerPoints
    let blockFrame = block.frame
    for line in block.lines {
        let lineText = line.text
        let lineConfidence = line.confidence
        let lineLanguages = line.recognizedLanguages
        let lineCornerPoints = line.cornerPoints
        let lineFrame = line.frame
        for element in line.elements {
            let elementText = element.text
            let elementConfidence = element.confidence
            let elementLanguages = element.recognizedLanguages
            let elementCornerPoints = element.cornerPoints
            let elementFrame = element.frame
        }
    }
}

Objective-C

NSString *resultText = result.text;
for (FIRVisionTextBlock *block in result.blocks) {
  NSString *blockText = block.text;
  NSNumber *blockConfidence = block.confidence;
  NSArray<FIRVisionTextRecognizedLanguage *> *blockLanguages = block.recognizedLanguages;
  NSArray<NSValue *> *blockCornerPoints = block.cornerPoints;
  CGRect blockFrame = block.frame;
  for (FIRVisionTextLine *line in block.lines) {
    NSString *lineText = line.text;
    NSNumber *lineConfidence = line.confidence;
    NSArray<FIRVisionTextRecognizedLanguage *> *lineLanguages = line.recognizedLanguages;
    NSArray<NSValue *> *lineCornerPoints = line.cornerPoints;
    CGRect lineFrame = line.frame;
    for (FIRVisionTextElement *element in line.elements) {
      NSString *elementText = element.text;
      NSNumber *elementConfidence = element.confidence;
      NSArray<FIRVisionTextRecognizedLanguage *> *elementLanguages = element.recognizedLanguages;
      NSArray<NSValue *> *elementCornerPoints = element.cornerPoints;
      CGRect elementFrame = element.frame;
    }
  }
}

下一步


識別文件圖像中的文字

若要識別文件的文本,請按如下所述配置並執行文件文字辨識器。

下面描述的文件文字辨識 API 提供了一個旨在更方便處理文件影像的介面。但是,如果您喜歡稀疏文字 API 提供的接口,則可以透過將雲端文字辨識器配置為使用密集文字模型來使用稀疏文字 API 來掃描文件。

使用文件文字識別API:

1. 運行文字辨識器

將影像作為UIImageCMSampleBufferRef傳遞給VisionDocumentTextRecognizerprocess(_:completion:)方法:

  1. 透過呼叫cloudDocumentTextRecognizer來取得VisionDocumentTextRecognizer的實例:

    迅速

    let vision = Vision.vision()
    let textRecognizer = vision.cloudDocumentTextRecognizer()
    
    // Or, to provide language hints to assist with language detection:
    // See https://cloud.google.com/vision/docs/languages for supported languages
    let options = VisionCloudDocumentTextRecognizerOptions()
    options.languageHints = ["en", "hi"]
    let textRecognizer = vision.cloudDocumentTextRecognizer(options: options)
    

    Objective-C

    FIRVision *vision = [FIRVision vision];
    FIRVisionDocumentTextRecognizer *textRecognizer = [vision cloudDocumentTextRecognizer];
    
    // Or, to provide language hints to assist with language detection:
    // See https://cloud.google.com/vision/docs/languages for supported languages
    FIRVisionCloudDocumentTextRecognizerOptions *options =
            [[FIRVisionCloudDocumentTextRecognizerOptions alloc] init];
    options.languageHints = @[@"en", @"hi"];
    FIRVisionDocumentTextRecognizer *textRecognizer = [vision cloudDocumentTextRecognizerWithOptions:options];
    
  2. 為了呼叫 Cloud Vision,映像必須格式化為 base64 編碼的字串。處理UIImage

    迅速

    guard let imageData = uiImage.jpegData(compressionQuality: 1.0) else { return }
    let base64encodedImage = imageData.base64EncodedString()

    Objective-C

    NSData *imageData = UIImageJPEGRepresentation(uiImage, 1.0f);
    NSString *base64encodedImage =
      [imageData base64EncodedStringWithOptions:NSDataBase64Encoding76CharacterLineLength];
  3. 然後,將影像傳遞給process(_:completion:)方法:

    迅速

    textRecognizer.process(visionImage) { result, error in
      guard error == nil, let result = result else {
        // ...
        return
      }
    
      // Recognized text
    }
    

    Objective-C

    [textRecognizer processImage:image
                      completion:^(FIRVisionDocumentText *_Nullable result,
                                   NSError *_Nullable error) {
      if (error != nil || result == nil) {
        // ...
        return;
      }
    
        // Recognized text
    }];
    

2. 從識別的文本區塊中提取文本

如果文字辨識操作成功,將會傳回一個VisionDocumentText物件。 VisionDocumentText物件包含影像中識別的全文以及反映已識別文件結構的物件層次結構:

對於每個VisionDocumentTextBlockVisionDocumentTextParagraphVisionDocumentTextWordVisionDocumentTextSymbol對象,您可以獲得該區域中識別的文本以及該區域的邊界座標。

例如:

迅速

let resultText = result.text
for block in result.blocks {
    let blockText = block.text
    let blockConfidence = block.confidence
    let blockRecognizedLanguages = block.recognizedLanguages
    let blockBreak = block.recognizedBreak
    let blockCornerPoints = block.cornerPoints
    let blockFrame = block.frame
    for paragraph in block.paragraphs {
        let paragraphText = paragraph.text
        let paragraphConfidence = paragraph.confidence
        let paragraphRecognizedLanguages = paragraph.recognizedLanguages
        let paragraphBreak = paragraph.recognizedBreak
        let paragraphCornerPoints = paragraph.cornerPoints
        let paragraphFrame = paragraph.frame
        for word in paragraph.words {
            let wordText = word.text
            let wordConfidence = word.confidence
            let wordRecognizedLanguages = word.recognizedLanguages
            let wordBreak = word.recognizedBreak
            let wordCornerPoints = word.cornerPoints
            let wordFrame = word.frame
            for symbol in word.symbols {
                let symbolText = symbol.text
                let symbolConfidence = symbol.confidence
                let symbolRecognizedLanguages = symbol.recognizedLanguages
                let symbolBreak = symbol.recognizedBreak
                let symbolCornerPoints = symbol.cornerPoints
                let symbolFrame = symbol.frame
            }
        }
    }
}

Objective-C

NSString *resultText = result.text;
for (FIRVisionDocumentTextBlock *block in result.blocks) {
  NSString *blockText = block.text;
  NSNumber *blockConfidence = block.confidence;
  NSArray<FIRVisionTextRecognizedLanguage *> *blockRecognizedLanguages = block.recognizedLanguages;
  FIRVisionTextRecognizedBreak *blockBreak = block.recognizedBreak;
  CGRect blockFrame = block.frame;
  for (FIRVisionDocumentTextParagraph *paragraph in block.paragraphs) {
    NSString *paragraphText = paragraph.text;
    NSNumber *paragraphConfidence = paragraph.confidence;
    NSArray<FIRVisionTextRecognizedLanguage *> *paragraphRecognizedLanguages = paragraph.recognizedLanguages;
    FIRVisionTextRecognizedBreak *paragraphBreak = paragraph.recognizedBreak;
    CGRect paragraphFrame = paragraph.frame;
    for (FIRVisionDocumentTextWord *word in paragraph.words) {
      NSString *wordText = word.text;
      NSNumber *wordConfidence = word.confidence;
      NSArray<FIRVisionTextRecognizedLanguage *> *wordRecognizedLanguages = word.recognizedLanguages;
      FIRVisionTextRecognizedBreak *wordBreak = word.recognizedBreak;
      CGRect wordFrame = word.frame;
      for (FIRVisionDocumentTextSymbol *symbol in word.symbols) {
        NSString *symbolText = symbol.text;
        NSNumber *symbolConfidence = symbol.confidence;
        NSArray<FIRVisionTextRecognizedLanguage *> *symbolRecognizedLanguages = symbol.recognizedLanguages;
        FIRVisionTextRecognizedBreak *symbolBreak = symbol.recognizedBreak;
        CGRect symbolFrame = symbol.frame;
      }
    }
  }
}

下一步