您可以使用 Firebase ML 识别图片中的文本。Firebase ML 既有可用于识别图片中的文本(例如街道标志的文本)的通用 API,也有针对识别文档文本而优化的 API。
准备工作
-
如果您尚未将 Firebase 添加到自己的应用中,请按照入门指南中的步骤进行添加。
- 在 Xcode 中打开您的应用项目,依次点击 File(文件)> Add Packages(添加软件包)。
- 出现提示时,添加 Firebase Apple 平台 SDK 代码库:
- 选择 Firebase ML 库。
- 将
-ObjC
标志添加到目标 build 设置的“其他链接器标志”部分。 - 完成之后,Xcode 将会自动开始在后台解析和下载您的依赖项。
- 在您的应用中导入 Firebase:
Swift
import FirebaseMLModelDownloader
Objective-C
@import FirebaseMLModelDownloader;
-
如果您尚未为项目启用云端 API,请立即完成以下操作:
- 打开 Firebase 控制台的 Firebase ML API 页面。
-
如果您尚未将项目升级到 Blaze 定价方案,请点击升级以执行此操作。(只有在您的项目未采用 Blaze 方案时,系统才会提示您进行升级。)
只有 Blaze 级项目才能使用基于 Cloud 的 API。
- 如果尚未启用基于 Cloud 的 API,请点击启用基于 Cloud 的 API。
使用 Swift Package Manager 安装和管理 Firebase 依赖项。
https://github.com/firebase/firebase-ios-sdk.git
接下来,执行一些应用内设置:
现在,您可以开始识别图片中的文本了。
输入图片指南
-
为了使 Firebase ML 准确识别文本,输入图片必须包含由足够像素数据表示的文本。理想情况下,对于拉丁文本,每个字符应至少为 16x16 像素。对于中文、日文和韩文文本,每个字符应为 24x24 像素。对于所有语言,字符像素大于 24x24 通常不会增加准确性。
例如,640x480 像素的图片可能非常适合用于扫描占据图片整个宽度的名片。如需扫描打印在信纸大小纸张上的文档,可能需要 720x1280 像素的图片。
-
图片聚焦不佳会影响文本识别的准确性。如果您无法获得满意的结果,请尝试让用户重新采集图片。
识别图片中的文本
如需识别图片中的文本,请按照以下说明运行文本识别器。
1. 运行文本识别器
将图片作为UIImage
或 CMSampleBufferRef
传递给 VisionTextRecognizer
的 process(_:completion:)
方法:
- 通过调用
cloudTextRecognizer
获取VisionTextRecognizer
的实例: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];
-
如需调用 Cloud Vision,图片的格式必须为 base64 编码字符串。如需处理
UIImage
,请执行以下操作: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];
- 然后,将图片传递给
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. 从识别出的文本块中提取文本
如果文本识别操作成功,它将返回一个VisionText
对象。VisionText
对象包含图片中识别到的完整文本以及零个或零个以上的 VisionTextBlock
对象。
每个 VisionTextBlock
表示一个矩形文本块,其中包含零个或零个以上的 VisionTextLine
对象。每个 VisionTextLine
对象包含零个或零个以上的 VisionTextElement
对象,这些对象表示字词和类似字词的实体(日期、数字等)。
对于每个 VisionTextBlock
、VisionTextLine
和 VisionTextElement
对象,您可以获取区域中识别出的文本以及该区域的边界坐标。
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; } } }
后续步骤
- 在向生产环境中部署使用 Cloud API 的应用之前,您应该执行一些额外的步骤来防止未经授权的 API 访问并减轻这些访问造成的影响。
识别文档图片中的文本
如需识别文档的文本,请按照以下说明配置并运行文档文本识别器。
下文所述的文档文本识别 API 提供了一个旨在更方便地处理文档图片的接口。但是,如果您更喜欢使用稀疏文本 API 提供的接口,则可以改用该接口来扫描文档(只需将云端文本识别器配置为使用密集文本模型即可)。
如需使用文档文本识别 API,请执行以下操作:
1. 运行文本识别器
将图片作为UIImage
或 CMSampleBufferRef
传递给 VisionDocumentTextRecognizer
的 process(_:completion:)
方法:
- 通过调用
cloudDocumentTextRecognizer
获取VisionDocumentTextRecognizer
的实例: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];
-
如需调用 Cloud Vision,图片的格式必须为 base64 编码字符串。如需处理
UIImage
,请执行以下操作: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];
- 然后,将图片传递给
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. 从识别出的文本块中提取文本
如果文本识别操作成功,它将返回一个VisionDocumentText
对象。VisionDocumentText
对象包含图片中识别到的完整文本以及反映所识别的文档结构的对象层次结构:
对于每个 VisionDocumentTextBlock
、VisionDocumentTextParagraph
、VisionDocumentTextWord
和 VisionDocumentTextSymbol
对象,您可以获取区域中识别出的文本以及该区域的边界坐标。
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; } } } }
后续步骤
- 在向生产环境中部署使用 Cloud API 的应用之前,您应该执行一些额外的步骤来防止未经授权的 API 访问并减轻这些访问造成的影响。