借助 ML Kit 的数字墨水识别功能,您可以识别数字平面上数百种语言的手写文本,还可以对草图进行分类。
试试看
- 您可以试用示例应用,了解此 API 的使用示例。
准备工作
在 Podfile 中添加以下机器学习套件库:
pod 'GoogleMLKit/DigitalInkRecognition', '8.0.0'
安装或更新项目的 Pod 之后,请使用 Xcode 项目的
.xcworkspace
来打开项目。Xcode 13.2.1 版或更高版本支持机器学习套件。
现在,您可以开始识别 Ink
对象中的文本了。
构建 Ink
对象
构建 Ink
对象的主要方法是在触摸屏上绘制。在 iOS 上,您可以使用 UIImageView 以及触控事件处理程序,这些处理程序会在屏幕上绘制笔画,还会存储笔画的点来构建 Ink
对象。以下代码段演示了这种一般模式。如需更完整的示例,请参阅快速入门应用,该示例将触控事件处理、屏幕绘制和笔画数据管理分开。
Swift
@IBOutlet weak var mainImageView: UIImageView! var kMillisecondsPerTimeInterval = 1000.0 var lastPoint = CGPoint.zero private var strokes: [Stroke] = [] private var points: [StrokePoint] = [] func drawLine(from fromPoint: CGPoint, to toPoint: CGPoint) { UIGraphicsBeginImageContext(view.frame.size) guard let context = UIGraphicsGetCurrentContext() else { return } mainImageView.image?.draw(in: view.bounds) context.move(to: fromPoint) context.addLine(to: toPoint) context.setLineCap(.round) context.setBlendMode(.normal) context.setLineWidth(10.0) context.setStrokeColor(UIColor.white.cgColor) context.strokePath() mainImageView.image = UIGraphicsGetImageFromCurrentImageContext() mainImageView.alpha = 1.0 UIGraphicsEndImageContext() } override func touchesBegan(_ touches: Set, with event: UIEvent?) { guard let touch = touches.first else { return } lastPoint = touch.location(in: mainImageView) let t = touch.timestamp points = [StrokePoint.init(x: Float(lastPoint.x), y: Float(lastPoint.y), t: Int(t * kMillisecondsPerTimeInterval))] drawLine(from:lastPoint, to:lastPoint) } override func touchesMoved(_ touches: Set , with event: UIEvent?) { guard let touch = touches.first else { return } let currentPoint = touch.location(in: mainImageView) let t = touch.timestamp points.append(StrokePoint.init(x: Float(currentPoint.x), y: Float(currentPoint.y), t: Int(t * kMillisecondsPerTimeInterval))) drawLine(from: lastPoint, to: currentPoint) lastPoint = currentPoint } override func touchesEnded(_ touches: Set , with event: UIEvent?) { guard let touch = touches.first else { return } let currentPoint = touch.location(in: mainImageView) let t = touch.timestamp points.append(StrokePoint.init(x: Float(currentPoint.x), y: Float(currentPoint.y), t: Int(t * kMillisecondsPerTimeInterval))) drawLine(from: lastPoint, to: currentPoint) lastPoint = currentPoint strokes.append(Stroke.init(points: points)) self.points = [] doRecognition() }
Objective-C
// Interface @property (weak, nonatomic) IBOutlet UIImageView *mainImageView; @property(nonatomic) CGPoint lastPoint; @property(nonatomic) NSMutableArray*strokes; @property(nonatomic) NSMutableArray *points; // Implementations static const double kMillisecondsPerTimeInterval = 1000.0; - (void)drawLineFrom:(CGPoint)fromPoint to:(CGPoint)toPoint { UIGraphicsBeginImageContext(self.mainImageView.frame.size); [self.mainImageView.image drawInRect:CGRectMake(0, 0, self.mainImageView.frame.size.width, self.mainImageView.frame.size.height)]; CGContextMoveToPoint(UIGraphicsGetCurrentContext(), fromPoint.x, fromPoint.y); CGContextAddLineToPoint(UIGraphicsGetCurrentContext(), toPoint.x, toPoint.y); CGContextSetLineCap(UIGraphicsGetCurrentContext(), kCGLineCapRound); CGContextSetLineWidth(UIGraphicsGetCurrentContext(), 10.0); CGContextSetRGBStrokeColor(UIGraphicsGetCurrentContext(), 1, 1, 1, 1); CGContextSetBlendMode(UIGraphicsGetCurrentContext(), kCGBlendModeNormal); CGContextStrokePath(UIGraphicsGetCurrentContext()); CGContextFlush(UIGraphicsGetCurrentContext()); self.mainImageView.image = UIGraphicsGetImageFromCurrentImageContext(); UIGraphicsEndImageContext(); } - (void)touchesBegan:(NSSet *)touches withEvent:(nullable UIEvent *)event { UITouch *touch = [touches anyObject]; self.lastPoint = [touch locationInView:self.mainImageView]; NSTimeInterval time = [touch timestamp]; self.points = [NSMutableArray array]; [self.points addObject:[[MLKStrokePoint alloc] initWithX:self.lastPoint.x y:self.lastPoint.y t:time * kMillisecondsPerTimeInterval]]; [self drawLineFrom:self.lastPoint to:self.lastPoint]; } - (void)touchesMoved:(NSSet *)touches withEvent:(nullable UIEvent *)event { UITouch *touch = [touches anyObject]; CGPoint currentPoint = [touch locationInView:self.mainImageView]; NSTimeInterval time = [touch timestamp]; [self.points addObject:[[MLKStrokePoint alloc] initWithX:currentPoint.x y:currentPoint.y t:time * kMillisecondsPerTimeInterval]]; [self drawLineFrom:self.lastPoint to:currentPoint]; self.lastPoint = currentPoint; } - (void)touchesEnded:(NSSet *)touches withEvent:(nullable UIEvent *)event { UITouch *touch = [touches anyObject]; CGPoint currentPoint = [touch locationInView:self.mainImageView]; NSTimeInterval time = [touch timestamp]; [self.points addObject:[[MLKStrokePoint alloc] initWithX:currentPoint.x y:currentPoint.y t:time * kMillisecondsPerTimeInterval]]; [self drawLineFrom:self.lastPoint to:currentPoint]; self.lastPoint = currentPoint; if (self.strokes == nil) { self.strokes = [NSMutableArray array]; } [self.strokes addObject:[[MLKStroke alloc] initWithPoints:self.points]]; self.points = nil; [self doRecognition]; }
请注意,此代码段包含一个示例函数,用于将笔画绘制到 UIImageView 中,您应根据需要针对自己的应用调整此函数。我们建议在绘制线段时使用圆角端点,这样零长度线段将绘制为点(想想小写字母 i 上的点)。doRecognition()
函数在每次写入笔画后调用,将在下文中定义。
获取 DigitalInkRecognizer
的实例
如需执行识别,我们需要将 Ink
对象传递给 DigitalInkRecognizer
实例。为了获得 DigitalInkRecognizer
实例,我们首先需要下载所需语言的识别器模型,并将该模型加载到 RAM 中。这可以使用以下代码段来实现,为简单起见,该代码段放置在 viewDidLoad()
方法中,并使用硬编码的语言名称。如需查看如何向用户显示可用语言列表并下载所选语言的示例,请参阅快速入门应用。
Swift
override func viewDidLoad() { super.viewDidLoad() let languageTag = "en-US" let identifier = DigitalInkRecognitionModelIdentifier(forLanguageTag: languageTag) if identifier == nil { // no model was found or the language tag couldn't be parsed, handle error. } let model = DigitalInkRecognitionModel.init(modelIdentifier: identifier!) let modelManager = ModelManager.modelManager() let conditions = ModelDownloadConditions.init(allowsCellularAccess: true, allowsBackgroundDownloading: true) modelManager.download(model, conditions: conditions) // Get a recognizer for the language let options: DigitalInkRecognizerOptions = DigitalInkRecognizerOptions.init(model: model) recognizer = DigitalInkRecognizer.digitalInkRecognizer(options: options) }
Objective-C
- (void)viewDidLoad { [super viewDidLoad]; NSString *languagetag = @"en-US"; MLKDigitalInkRecognitionModelIdentifier *identifier = [MLKDigitalInkRecognitionModelIdentifier modelIdentifierForLanguageTag:languagetag]; if (identifier == nil) { // no model was found or the language tag couldn't be parsed, handle error. } MLKDigitalInkRecognitionModel *model = [[MLKDigitalInkRecognitionModel alloc] initWithModelIdentifier:identifier]; MLKModelManager *modelManager = [MLKModelManager modelManager]; [modelManager downloadModel:model conditions:[[MLKModelDownloadConditions alloc] initWithAllowsCellularAccess:YES allowsBackgroundDownloading:YES]]; MLKDigitalInkRecognizerOptions *options = [[MLKDigitalInkRecognizerOptions alloc] initWithModel:model]; self.recognizer = [MLKDigitalInkRecognizer digitalInkRecognizerWithOptions:options]; }
快速入门应用包含额外的代码,用于演示如何同时处理多个下载,以及如何通过处理完成通知来确定哪些下载成功。
识别 Ink
对象
接下来,我们来看一下 doRecognition()
函数,为简单起见,该函数是从 touchesEnded()
调用的。在其他应用中,可能希望仅在超时后或用户按下按钮来触发识别时才调用识别。
Swift
func doRecognition() { let ink = Ink.init(strokes: strokes) recognizer.recognize( ink: ink, completion: { [unowned self] (result: DigitalInkRecognitionResult?, error: Error?) in var alertTitle = "" var alertText = "" if let result = result, let candidate = result.candidates.first { alertTitle = "I recognized this:" alertText = candidate.text } else { alertTitle = "I hit an error:" alertText = error!.localizedDescription } let alert = UIAlertController(title: alertTitle, message: alertText, preferredStyle: UIAlertController.Style.alert) alert.addAction(UIAlertAction(title: "OK", style: UIAlertAction.Style.default, handler: nil)) self.present(alert, animated: true, completion: nil) } ) }
Objective-C
- (void)doRecognition { MLKInk *ink = [[MLKInk alloc] initWithStrokes:self.strokes]; __weak typeof(self) weakSelf = self; [self.recognizer recognizeInk:ink completion:^(MLKDigitalInkRecognitionResult *_Nullable result, NSError *_Nullable error) { typeof(weakSelf) strongSelf = weakSelf; if (strongSelf == nil) { return; } NSString *alertTitle = nil; NSString *alertText = nil; if (result.candidates.count > 0) { alertTitle = @"I recognized this:"; alertText = result.candidates[0].text; } else { alertTitle = @"I hit an error:"; alertText = [error localizedDescription]; } UIAlertController *alert = [UIAlertController alertControllerWithTitle:alertTitle message:alertText preferredStyle:UIAlertControllerStyleAlert]; [alert addAction:[UIAlertAction actionWithTitle:@"OK" style:UIAlertActionStyleDefault handler:nil]]; [strongSelf presentViewController:alert animated:YES completion:nil]; }]; }
管理模型下载
我们已经了解了如何下载识别模型。以下代码段展示了如何检查模型是否已下载,或者在不再需要模型时将其删除以回收存储空间。
检查模型是否已下载
Swift
let model : DigitalInkRecognitionModel = ... let modelManager = ModelManager.modelManager() modelManager.isModelDownloaded(model)
Objective-C
MLKDigitalInkRecognitionModel *model = ...; MLKModelManager *modelManager = [MLKModelManager modelManager]; [modelManager isModelDownloaded:model];
删除下载的模型
Swift
let model : DigitalInkRecognitionModel = ... let modelManager = ModelManager.modelManager() if modelManager.isModelDownloaded(model) { modelManager.deleteDownloadedModel( model!, completion: { error in if error != nil { // Handle error return } NSLog(@"Model deleted."); }) }
Objective-C
MLKDigitalInkRecognitionModel *model = ...; MLKModelManager *modelManager = [MLKModelManager modelManager]; if ([self.modelManager isModelDownloaded:model]) { [self.modelManager deleteDownloadedModel:model completion:^(NSError *_Nullable error) { if (error) { // Handle error. return; } NSLog(@"Model deleted."); }]; }
提高文字识别准确度的技巧
文本识别的准确性可能因语言而异。准确性还取决于写作风格。虽然数字墨迹识别功能经过训练,可以处理多种书写风格,但结果可能会因用户而异。
以下是一些可提高文本识别器准确性的方法。请注意,这些技巧不适用于表情符号、自动绘图和形状的绘图分类器。
书写区域
许多应用都有明确定义的用户输入书写区域。符号的含义部分取决于其相对于包含它的书写区域的大小。 例如,小写字母“o”或“c”与大写字母“O”或“C”之间的区别,以及逗号与正斜杠之间的区别。
告知识别器书写区域的宽度和高度可以提高准确性。不过,识别器会假定书写区域仅包含一行文字。如果实际书写区域足够大,可供用户书写两行或更多行,那么您可以传入一个高度为单行文字高度的最佳估计值的 WritingArea,从而获得更好的结果。您传递给识别器的 WritingArea 对象不必与屏幕上的实际书写区域完全对应。以这种方式更改 WritingArea 高度在某些语言中比在其他语言中效果更好。
指定书写区域时,请以与笔画坐标相同的单位指定其宽度和高度。x、y 坐标实参没有单位要求 - API 会对所有单位进行归一化,因此唯一重要的是笔画的相对大小和位置。您可以根据系统的实际情况,随意传入任何比例的坐标。
前置上下文
预先上下文是指您尝试识别的 Ink
中紧邻笔画之前的文字。您可以通过告知识别器有关预上下文的信息来帮助识别器。
例如,草书字母“n”和“u”经常被混淆。如果用户已输入部分字词“arg”,则可以继续输入可识别为“ument”或“nment”的笔画。指定前上下文“arg”可消除歧义,因为“argument”一词比“argnment”更常见。
预上下文还可以帮助识别器识别字词分隔符(字词之间的空格)。您可以输入空格字符,但无法绘制空格字符,那么识别器如何确定一个字词何时结束,下一个字词何时开始?如果用户已经写了“hello”,然后继续写“world”,在没有预设上下文的情况下,识别器会返回字符串“world”。不过,如果您指定前置上下文“hello”,模型将返回字符串“ world”(带有前导空格),因为“hello world”比“helloword”更有意义。
您应提供尽可能长的预上下文字符串,最多 20 个字符(包括空格)。如果字符串更长,识别器只会使用最后 20 个字符。
以下代码示例展示了如何定义书写区域并使用 RecognitionContext
对象指定预上下文。
Swift
let ink: Ink = ...; let recognizer: DigitalInkRecognizer = ...; let preContext: String = ...; let writingArea = WritingArea.init(width: ..., height: ...); let context: DigitalInkRecognitionContext.init( preContext: preContext, writingArea: writingArea); recognizer.recognizeHandwriting( from: ink, context: context, completion: { (result: DigitalInkRecognitionResult?, error: Error?) in if let result = result, let candidate = result.candidates.first { NSLog("Recognized \(candidate.text)") } else { NSLog("Recognition error \(error)") } })
Objective-C
MLKInk *ink = ...; MLKDigitalInkRecognizer *recognizer = ...; NSString *preContext = ...; MLKWritingArea *writingArea = [MLKWritingArea initWithWidth:... height:...]; MLKDigitalInkRecognitionContext *context = [MLKDigitalInkRecognitionContext initWithPreContext:preContext writingArea:writingArea]; [recognizer recognizeHandwritingFromInk:ink context:context completion:^(MLKDigitalInkRecognitionResult *_Nullable result, NSError *_Nullable error) { NSLog(@"Recognition result %@", result.candidates[0].text); }];
笔画顺序
识别准确度对笔画顺序非常敏感。识别器希望笔画按照人们自然书写的顺序进行,例如英语是从左到右。任何偏离此模式的情况(例如,从最后一个字开始写英语句子)都会导致结果不太准确。
另一个示例是,当 Ink
中间的某个字词被移除并替换为另一个字词时。修订内容可能位于句子的中间,但修订的笔画位于笔画序列的末尾。在这种情况下,我们建议您将新写入的字词单独发送到 API,并使用您自己的逻辑将结果与之前的识别结果合并。
处理不明确的形状
在某些情况下,提供给识别器的形状的含义并不明确。例如,边角非常圆润的矩形可以被视为矩形或椭圆。
如果存在这些不明确的情况,可以使用识别得分来处理。只有形状分类器提供得分。如果模型非常有把握,那么最佳结果的分数会远高于次佳结果。如果存在不确定性,前两项结果的分数将非常接近。另请注意,形状分类器会将整个 Ink
视为单个形状。例如,如果 Ink
包含一个矩形和一个相邻的椭圆,识别器可能会返回其中一个(或完全不同的内容)作为结果,因为单个识别候选对象无法表示两种形状。