您可以使用 Firebase ML 識別圖像中的文本。 Firebase ML 既有適合識別圖像中文本（例如路標文本）的通用 API，也有針對識別文檔文本而優化的 API。
在你開始之前
- 如果您尚未將 Firebase 添加到您的應用中，請按照入門指南中的步驟進行操作。
- 在 Xcode 中，打開應用程序項目，導航至File > Add Packages 。
- 出現提示時，添加 Firebase Apple 平台 SDK 存儲庫：
- 選擇 Firebase ML 庫。
- 完成後，Xcode 將自動開始在後台解析並下載您的依賴項。
- 在您的應用中，導入 Firebase：
迅速
import FirebaseMLModelDownloader
Objective-C
@import FirebaseMLModelDownloader;
如果您尚未為您的項目啟用基於雲的 API，請立即執行此操作：
- 打開 Firebase 控制台的Firebase ML API 頁面。
如果您尚未將項目升級到 Blaze 定價計劃，請單擊升級來執行此操作。 （僅當您的項目不在 Blaze 計劃中時，系統才會提示您升級。）
只有 Blaze 級別的項目才能使用基於雲的 API。
- 如果尚未啟用基於雲的 API，請單擊啟用基於雲的 API 。
使用 Swift Package Manager 安裝和管理 Firebase 依賴項。
https://github.com/firebase/firebase-ios-sdk
接下來，執行一些應用內設置：
現在您已準備好開始識別圖像中的文本。
輸入圖像指南
為了讓 Firebase ML 準確識別文本，輸入圖像必須包含由足夠的像素數據表示的文本。理想情況下，對於拉丁文本，每個字符應至少為 16x16 像素。對於中文、日文和韓文文本，每個字符應為 24x24 像素。對於所有語言，大於 24x24 像素的字符通常不會帶來準確性優勢。
例如，640x480 的圖像可能適合掃描佔據圖像整個寬度的名片。要掃描打印在 letter 尺寸紙張上的文檔，可能需要 720x1280 像素的圖像。
圖像焦點不佳會損害文本識別的準確性。如果您沒有獲得可接受的結果，請嘗試要求用戶重新捕獲圖像。
識別圖像中的文本
要識別圖像中的文本，請按如下所述運行文本識別器。
1. 運行文本識別器
將圖像作為UIImage或CMSampleBufferRef傳遞給VisionTextRecognizer的process(_:completion:)方法：- 通過調用
cloudTextRecognizer獲取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]; - 為了調用 Cloud Vision，圖像必須格式化為 base64 編碼的字符串。處理
UIImage：迅速
guard let imageData = uiImage.jpegData(compressionQuality: 1.0f) else { return } let base64encodedImage = imageData.base64EncodedString()Objective-C
NSData *imageData = UIImageJPEGRepresentation(uiImage, 1.0f); NSString *base64encodedImage = [imageData base64EncodedStringWithOptions:NSDataBase64Encoding76CharacterLineLength];
- 然後，將圖像傳遞給
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. 從識別的文本塊中提取文本
如果文本識別操作成功，將返回一個VisionText對象。 VisionText對象包含圖像中識別的全文以及零個或多個VisionTextBlock對象。每個VisionTextBlock代表一個矩形文本塊，其中包含零個或多個VisionTextLine對象。每個VisionTextLine對象包含零個或多個VisionTextElement對象，這些對象表示單詞和類似單詞的實體（日期、數字等）。
對於每個VisionTextBlock 、 VisionTextLine和VisionTextElement對象，您可以獲得該區域中識別的文本以及該區域的邊界坐標。
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
- 在將使用雲 API 的應用程序部署到生產環境之前，您應該採取一些額外的步驟來防止和減輕未經授權的 API 訪問的影響。
識別文檔圖像中的文本
要識別文檔的文本，請按如下所述配置並運行文檔文本識別器。
下面描述的文檔文本識別 API 提供了一個旨在更方便地處理文檔圖像的接口。但是，如果您更喜歡稀疏文本 API 提供的接口，則可以通過將雲文本識別器配置為使用密集文本模型來使用稀疏文本 API 來掃描文檔。
使用文檔文本識別API：
1. 運行文本識別器
將圖像作為UIImage或CMSampleBufferRef傳遞給VisionDocumentTextRecognizer的process(_:completion:)方法：- 通過調用
cloudDocumentTextRecognizer獲取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]; - 為了調用 Cloud Vision，圖像必須格式化為 base64 編碼的字符串。處理
UIImage：迅速
guard let imageData = uiImage.jpegData(compressionQuality: 1.0f) else { return } let base64encodedImage = imageData.base64EncodedString()Objective-C
NSData *imageData = UIImageJPEGRepresentation(uiImage, 1.0f); NSString *base64encodedImage = [imageData base64EncodedStringWithOptions:NSDataBase64Encoding76CharacterLineLength];
- 然後，將圖像傳遞給
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. 從識別的文本塊中提取文本
如果文本識別操作成功，將返回一個VisionDocumentText對象。 VisionDocumentText對象包含圖像中識別的全文以及反映已識別文檔結構的對象層次結構：對於每個VisionDocumentTextBlock 、 VisionDocumentTextParagraph 、 VisionDocumentTextWord和VisionDocumentTextSymbol對象，您可以獲得該區域中識別的文本以及該區域的邊界坐標。
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
- 在將使用雲 API 的應用程序部署到生產環境之前，您應該採取一些額外的步驟來防止和減輕未經授權的 API 訪問的影響。