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。
- 在 Xcode 中,開啟應用程式項目,導覽至File > Add Packages 。
- 出現提示時,新增 Firebase Apple 平台 SDK 儲存庫:
- 選擇 Firebase ML 庫。
- 將
-ObjC
標誌新增至目標建置設定的「其他連結器標誌」部分。 - 完成後,Xcode 將自動開始在背景解析並下載您的依賴項。
- 在您的應用程式中,導入 Firebase:
迅速
import FirebaseMLModelDownloader
Objective-C
@import FirebaseMLModelDownloader;
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:
- 開啟 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。
- 如果尚未啟用基於雲端的 API,請按一下啟用基於雲端的 API 。
使用 Swift Package Manager 安裝和管理 Firebase 相依性。
https://github.com/firebase/firebase-ios-sdk.git
接下來,執行一些應用程式內設定:
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UIImage
或CMSampleBufferRef
傳遞給VisionTextRecognizer
的process(_:completion:)
方法:- 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];
- 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];
- 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VisionTextBlock
、 VisionTextLine
和VisionTextElement
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
- 在將使用雲端 API 的應用程式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 存取的影響。
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UIImage
或CMSampleBufferRef
傳遞給VisionDocumentTextRecognizer
的process(_:completion:)
方法:- 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];
- 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];
- 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
- 在將使用雲端 API 的應用程式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 存取的影響。