Azure认知服务之使用墨迹识别功能识别手写汉字

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功能，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":{"height":32."class":"leaf","id":8,"parent
...

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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