借助机器学习套件的数字手写识别功能,您可以识别在数字表面上以数百种语言手写的文字,并对草图进行分类。
试试看
- 请玩转示例应用,查看此 API 的示例用法。
准备工作
在 Podfile 中添加以下机器学习套件库:
pod 'GoogleMLKit/DigitalInkRecognition', '3.2.0'
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 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该对象。在 iOS 上,您可以使用 UIImageView 和触摸事件处理脚本,用于在屏幕上绘制描边,还会存储描边的点,以构建 Ink
对象。以下代码段演示了这种常规模式。如需查看更完整的示例,请参阅快速入门应用。该示例将触摸事件处理、屏幕绘制和描边数据管理分开。
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,该代码段包含一个示例函数,用于将描边绘制到 UIImageView 中,该函数应根据您的应用进行必要的调整。我们建议在绘制线段时使用圆角,以便将零长度的线段绘制为点(以小写字母 i 中的点为例)。系统会在每条笔画写入后调用 doRecognition()
函数,其定义如下所述。
获取 DigitalInkRecognizer
的实例
为了执行识别,我们需要将 Ink
对象传递给 DigitalInkRecognizer
实例。为了获取 DigitalInkRecognizer
实例,我们首先需要下载所需语言的识别器模型,并将模型加载到 RAM 中。这可以使用以下代码段来实现,为简单起见,我们将其放置在了 viewDidLoad()
方法中,并使用硬编码的语言名称。请参阅快速入门应用,通过示例了解如何向用户显示可用语言列表并下载所选语言。
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,展示了如何同时处理多项下载,以及如何通过处理完成通知来确定哪项下载成功完成。
识别 Ink
对象
接下来是 doRecognition()
函数,为简单起见,该函数从 touchesEnded()
调用。在其他应用中,可能希望在超时后或在用户按下按钮触发识别时调用识别。
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。以下代码段说明了如何检查模型是否已下载,或在不再需要模型来恢复存储空间时将其删除。
检查模型是否已经下载
Swift
let model : DigitalInkRecognitionModel = ... let modelManager = ModelManager.modelManager() modelManager.isModelDownloaded(model)
Objective-C
MLKDigitalInkRecognitionModel *model = ...; MLKModelManager *modelManager = [MLKModelManager modelManager]; [modelManager isModelDownloaded:model];
删除已下载的模型
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."); }]; }
提高文字识别准确性的技巧
文本识别的准确性可能因语言而异。准确性还取决于书写风格虽然数字墨水识别经过训练后可以处理多种书写风格,但结果可能会因用户而异。
以下是一些可以提高文本识别器准确性的方法。请注意,这些方法不适用于表情符号、自动绘制和形状的绘制分类器。
书写区域
许多应用都有明确定义的写入区域,供用户输入。符号的含义部分取决于符号的大小(相对于包含该符号的书写区域的大小)。例如,小写或大写字母“o”或“c”以及英文逗号与正斜杠之间的区别。
让识别器知道书写区域的宽度和高度可以提高准确性。但是,识别器会假设书写区域仅包含一行文本。如果实际书写区域足够大,允许用户书写两行或更多行,那么您可以通过传入一个 writingArea,其中的高度是对单行文本高度的最佳估算值,这样可以获得更好的结果。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一致。以这种方式更改 WritingArea 高度在某些语言中效果要好于其他语言。
指定书写区域时,请指定其宽度和高度(采用与描边坐标相同的单位)。x,y 坐标参数没有单位要求 - API 会对所有单位进行标准化,因此唯一重要的是描边的相对大小和位置。您可以随意传递对您的系统有意义的任何比例的坐标。
预先提供上下文
预上下文是指您尝试识别的 Ink
中笔画前面的文本。您可以通过告知识别器预先上下文来帮助识别器。
例如,书写字母“n”和“u”经常被混淆。如果用户已输入部分单词“arg”,他们可能会继续以可被识别为“ument”或“nment”的笔画。指定预上下文“arg”可解决不明确的问题,因为“argument”一词可能比“argnment”。
预上下文还有助于识别器识别断字,即字词之间的空格。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?如果用户已经输入了“hello”,然后继续输入单词“world”,在没有预先上下文的情况下,识别器会返回字符串“world”。但是,如果您指定预先上下文“hello”,模型将返回字符串“world”和前导空格,因为“hello world”比“helloword”更有意义。
您应该提供尽可能长的预上下文字符串,最多为 20 个字符,包括空格。如果字符串更长,则识别器仅使用最后 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); }];
描边排序
识别准确度受笔画顺序的影响。识别器会预期笔画按照人们自然书写的顺序出现;例如,对于英语,书写顺序从左到右。任何偏离此模式的大小写(例如以最后一个单词开始的英文句子)都会导致结果不太准确。
另一个示例是,移除 Ink
中间的某个单词并将其替换为其他单词。修订可能在句子的中间,但修订的笔画在笔画序列的结尾。在这种情况下,我们建议将新写好的单词单独发送到 API,并使用您自己的逻辑将结果与之前的识别合并起来。
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不明确。例如,边缘非常圆的矩形可以看作是矩形或椭圆形。
对于这些模糊不清的情况,可以使用识别分数(如果有)来处理。只有形状分类器提供分数。如果模型的置信度非常高,则得分最高的结果的得分会比次优结果高得多。如果不确定性,前两个结果的分数将非常接近。另请注意,形状分类器会将整个 Ink
解读为单个形状。例如,如果 Ink
包含彼此相邻的矩形和椭圆形,则识别器可能会返回二者之一(或完全不同的内容),因为一个识别候选网络不能表示两种形状。