前面我们使用Azure Face实现了人脸识别、使用Azure表格识别器提取了表格里的数据。这次我们试试使用Azure墨迹识别API来对笔迹进行识别。

墨迹识别

墨迹识别器认知服务提供基于云的 REST API 用于分析和识别数字墨迹内容。与使用光学字符识别 (OCR) 的服务不同，该 API 需要使用数字墨迹笔划数据作为输入。数字墨迹笔划是 2D 点（X,Y 坐标，表示数字手写笔或手指的动作）的时序集。然后，墨迹识别器会识别输入中的形状和手写内容，并返回包含所有已识别实体的 JSON 响应。

引用自微软文档

它不是ocr对图像进行识别，而是对墨迹数据进行识别。墨迹数据的原理主要是一些手写输入设备，比如平板，手写板等。

创建墨迹识别资源

跟前面的内容一样，在portal控制台找到墨迹识别，点击创建，取一个实例名。墨迹识别也是一个免费服务，定价选F0方案，额度为5次/分，20000事务/月。

获取秘钥和终结点

我们调用墨迹识别API需要秘钥跟终结点信息。点击菜单“密钥和终结点”查看信息。

新建一个WPF项目

我们这次同样实现一个WPF小程序。界面上放置一个InkCanvas用来手写，一个文本框用来显示识别的文本，一个按钮用来触发识别。

MainWindow.xaml

修改MainWindow.xaml为如下代码：

<Window x:Class="InkRec2.MainWindow"xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"xmlns:d="http://schemas.microsoft.com/expression/blend/2008"xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006"mc:Ignorable="d"xmlns:local="clr-namespace:NoteTaker"xmlns:controls="clr-namespace:Microsoft.Toolkit.Wpf.UI.Controls;assembly=Microsoft.Toolkit.Wpf.UI.Controls"Title="MainWindow"><Grid ><Grid.RowDefinitions><RowDefinition Height="4*" /><RowDefinition Height="1*" /><RowDefinition Height="50" /></Grid.RowDefinitions><Border Grid.Row ="0" BorderBrush="Black" BorderThickness="1"><controls:InkCanvas x:Name="inkCanvas" Loaded="inkCanvas_Loaded"/></Border><Border Grid.Row ="1" BorderBrush="Black" BorderThickness="1"><ScrollViewer><TextBox x:Name="output" FontSize="18" TextWrapping="Wrap"/></ScrollViewer></Border><StackPanel Grid.Row="2" Orientation="Horizontal"><Button Click="Button_InkRec">开始识别</Button></StackPanel></Grid>
</Window>

注意：InkCanvas控件需要使用的是Microsoft.Toolkit.Wpf.UI.Controls包下的，如果本地没有使用nuget进行安装

采集墨迹

inkCanvas load事件里设置输入设备的类型：

   private void inkCanvas_Loaded(object sender, RoutedEventArgs e){inkCanvas.InkPresenter.InputDeviceTypes = CoreInputDeviceTypes.Mouse | CoreInputDeviceTypes.Pen | CoreInputDeviceTypes.Touch;}

先定义几个模型用来存储墨迹数据：

    public class InkStroke{public int id { get; set; }public string points { get; set; }}public class InkData{public string language { get; set; }public List<InkStroke> strokes { get; set; }}

从InkCanvas获取墨迹数据组装成InkData：

        private InkData GetInkData(){var data = new InkData();data.language = "zh-CN";data.strokes = new List<InkStroke>();int id = 0;foreach (var stroke in this.inkCanvas.InkPresenter.StrokeContainer.GetStrokes()){var points = stroke.GetInkPoints();var convertPoints = ConvertPixelsToMillimeters(points);var inkStorke = new InkStroke();inkStorke.id = id++;var sb = new StringBuilder();foreach (var point in convertPoints){sb.Append(point.X);sb.Append(",");sb.Append(point.Y);sb.Append(",");}inkStorke.points = sb.ToString().TrimEnd(',');data.strokes.Add(inkStorke);}return data;}private List<System.Windows.Point> ConvertPixelsToMillimeters(IReadOnlyList<InkPoint> pointsInPixels){float dpiX = 96.0f;float dpiY = 96.0f;var transformedInkPoints = new List<System.Windows.Point>();const float inchToMillimeterFactor = 25.4f;foreach (var point in pointsInPixels){var transformedX = (point.Position.X / dpiX) * inchToMillimeterFactor;var transformedY = (point.Position.Y / dpiY) * inchToMillimeterFactor;transformedInkPoints.Add(new System.Windows.Point(transformedX, transformedY));}return transformedInkPoints;}

调用墨迹API

这里需要前面复制好的密钥跟终结点地址。识别其实很简单，就是把墨迹数据转换成json后给服务器发生一个put请求，识别成功后就会返回一个json字符串的结果。

        private async Task<string> InkRec(InkData data){string inkRecognitionUrl = "/inkrecognizer/v1.0-preview/recognize";string endPoint = "x";string subscriptionKey = "x";using (HttpClient client = new HttpClient { BaseAddress = new Uri(endPoint) }){System.Net.ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12 | SecurityProtocolType.Tls11 | SecurityProtocolType.Tls;client.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json"));client.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", subscriptionKey);var jsonData = JsonConvert.SerializeObject(data);var content = new StringContent(jsonData, Encoding.UTF8, "application/json");var res = await client.PutAsync(inkRecognitionUrl, content);if (res.IsSuccessStatusCode){var result = await res.Content.ReadAsStringAsync();return result;}else{var err = $"ErrorCode: {res.StatusCode}";return err;}}}

解析识别结果

识别成功后，结果会以json字符串的形式进行返回。结果是一个数组，里面存放了每一个笔迹的识别结果，以及最终的识别结果。
结果示例：

{"recognitionUnits":[{"alternates":[{"category":"inkWord","recognizedString":"乖"},{"category":"inkWord","recognizedString":"黍"},{"category":"inkWord","recognizedString":"秉"},{"category":"inkWord","recognizedString":"乗"},{"category":"inkWord","recognizedString":"埀"}],"boundingRectangle":{"height":48.159999847412109,"topX":7.190000057220459,"topY":22.010000228881836,"width":35.639999389648438},"category":"inkWord","class":"leaf","id":4,"parentId":3,"recognizedText":"乘","rotatedBoundingRectangle":[{"x":41.490001678466797,"y":21.25},{"x":43.209999084472656,"y":69.239997863769531},{"x":7.8299999237060547,"y":70.5},{"x":6.1100001335144043,"y":22.520000457763672}],"strokeIds":[0,1,2,3,4,5,6,7,8,9]},{"alternates":[{"category":"inkWord","recognizedString":"風"},{"category":"inkWord","recognizedString":"夙"},{"category":"inkWord","recognizedString":"凤"},{"category":"inkWord","recognizedString":"凡"},{"category":"inkWord","recognizedString":"㶡"}],"boundingRectangle":
...
...

有了结果那么我们只要对其进行反序列化取出想要的识别结果就行了。

    public class InkRecResponse{public List<InkRecResponseUnit> recognitionUnits { get; set; }}public class InkRecResponseUnit{public string category { get; set; }public string recognizedText { get; set; }}private async void Button_InkRec(object sender, RoutedEventArgs e){var inkData = GetInkData();var response = await InkRec(inkData);var jsonObj = JsonConvert.DeserializeObject<InkRecResponse>(response);var recognizedText = jsonObj.recognitionUnits.First(o => o.category == "line").recognizedText;this.output.Text = recognizedText;}

运行一下

我们的程序写好了，运行一下。在canvas上随便写上几个汉字点击识别按钮。字虽然丑了点，但是结果还是完美的。

总结

使用Azure墨迹识别可以轻松的识别手写输入设备的笔迹。墨迹识别功能并不是见到的orc识别，它可以对每一个笔画进行识别，提供候选结果。以上代码虽然多，其实主要是获取墨迹数据比较麻烦，其实真正识别墨迹只是一个http put请求而已，这是非常简单的。有了这个API我们可以实现很多创意，比如稍微改进下上面的代码就可以实现手写文字的连续识别功能，一边写一边不断的识别，封装进平板就是一款可以实时识别手写板啦。

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