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本頁面由 Cloud Translation API 翻譯而成。

在 iOS 上使用 Firebase ML 來辨識圖片中的文字

您可以使用 Firebase ML 辨識圖片中的文字。Firebase ML擁有適用於辨識圖片中的文字標誌文字，以及最佳化的 API，可辨識文件。

，瞭解如何調查及移除這項存取權。

事前準備

使用 Swift Package Manager 安裝及管理 Firebase 依附元件。

在 Xcode 中保持開啟應用程式專案，然後前往「檔案」檔案 >新增套件。
在系統提示時，新增 Firebase Apple 平台 SDK 存放區：

  https://github.com/firebase/firebase-ios-sdk.git

選擇 Firebase ML 程式庫。
在目標建構設定的「Other Linker Flags」部分中新增 -ObjC 標記。
完成後，Xcode 會自動開始解析並下載複製到背景依附元件

接著，進行一些應用程式內設定：

在應用程式中匯入 Firebase：

Swift

import FirebaseMLModelDownloader

Objective-C

@import FirebaseMLModelDownloader;

如果您尚未為專案啟用雲端式 API，請先啟用現在：
1. 開啟 Firebase ML Firebase 控制台的 API 頁面。
2. 如果您尚未將專案升級至 Blaze 定價方案，請按一下如要這麼做，請升級。(只有在您的專案並未採用 Blaze 方案)。
  
  只有 Blaze 層級的專案可以使用以雲端為基礎的 API。
3. 如果尚未啟用雲端式 API，請按一下「Enable Cloud-based API」(啟用雲端式 API) API
，瞭解如何調查及移除這項存取權。
部署至使用 Cloud API 的正式版應用程式之前，您應先完成防範及降低

現在可以開始辨識圖片中的文字。

輸入圖片規範

為了讓 Firebase ML 準確辨識文字，輸入圖片必須包含以充足的像素資料表示的文字最適合拉丁字母每個字元至少要有 16x16 像素中文日文和韓文文字字元應為 24x24 像素所有語言通常沒有對字元大於 24x24 像素的特性來說，準確性的優勢在於。

舉例來說，640x480 的圖片適合掃描名片圖片會佔滿圖片的整個寬度如何掃描列印的文件則建議使用 720x1280 像素的圖片。
圖片焦點不佳可能會降低文字辨識的準確度。如果您不請嘗試重新擷取圖片。

辨識圖片中的文字

如要辨識圖片中的文字，請按照說明執行文字辨識工具。

1. 執行文字辨識工具

將圖片做為 UIImage 或 CMSampleBufferRef 傳遞至 VisionTextRecognizer的process(_:completion:) 方法：

呼叫即可取得 VisionTextRecognizer 的例項 cloudTextRecognizer:

Swift

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];

為了呼叫 Cloud Vision，圖片必須採用 Base64 編碼格式字串。如何處理 UIImage：

Swift

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];

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 {
    // ...
    return
  }

  // Recognized text
}

Objective-C

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

  // Recognized text
}];

2. 從已辨識的文字區塊擷取文字

如果文字辨識作業成功，系統會傳回 VisionText 物件。VisionText 物件包含完整的文字已辨識於圖中，且為零或多個 VisionTextBlock 如需儲存大量結構化物件建議使用 Cloud Bigtable

每個 VisionTextBlock 都代表一段文字區塊，其中包含零或多個 VisionTextLine 物件。每VisionTextLine 物件包含零個或多個 VisionTextElement 物件，用來表示字詞和類似文字的實體 (日期、數字等)。

針對每個 VisionTextBlock、VisionTextLine 和 VisionTextElement 物件即可讓系統辨識該區域中的文字和區域。

例如：

Swift

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;
    }
  }
}

後續步驟

部署至使用 Cloud API 的正式版應用程式之前，您應先完成防範及減少未經授權 API 存取的影響

辨識文件圖片中的文字

如要辨識文件中的文字，請設定並執行與文件文字辨識工具搭配使用

以下說明文件文字辨識 API 提供的介面是為了方便處理文件圖片。不過如果您偏好稀疏文字 API 提供的介面，則可以使用這個 API 只要將 Cloud 文字辨識工具設為使用密集文字模型。

如何使用文件文字辨識 API：

1. 執行文字辨識工具

將圖片做為 UIImage 或 CMSampleBufferRef 傳遞至 VisionDocumentTextRecognizer的process(_:completion:) 方法：

呼叫即可取得 VisionDocumentTextRecognizer 的例項 cloudDocumentTextRecognizer:

Swift

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];

為了呼叫 Cloud Vision，圖片必須採用 Base64 編碼格式字串。如何處理 UIImage：

Swift

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];

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 {
    // ...
    return
  }

  // Recognized text
}

Objective-C

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

    // Recognized text
}];

2. 從已辨識的文字區塊擷取文字

如果文字辨識作業成功，系統會傳回 VisionDocumentText 物件。VisionDocumentText 物件包含圖片中可辨識的完整文字和物件階層反映公認文件的結構：

每 VisionDocumentTextBlock、VisionDocumentTextParagraph， VisionDocumentTextWord 和 VisionDocumentTextSymbol 物件，您可以取得可在區域辨識的文字和區域的邊界座標。

例如：

Swift

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;
      }
    }
  }
}

後續步驟

部署至使用 Cloud API 的正式版應用程式之前，您應先完成防範及減少未經授權 API 存取的影響