该代码通过训练一个分类器来识别手写数字，并在测试数据上进行评估，最后使用该分类器来识别一个数字验证码。注意：在运行此代码之前，需要安装OpenCV和ML.NET库。

using System;
using System.IO;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using OpenCvSharp;
using OpenCvSharp.Extensions;

namespace DigitRecognition
{
    class Program
    {
        static void Main(string[] args)
        {
            // Load training data
            var mlContext = new MLContext(seed: 0);
            var data = mlContext.Data.LoadFromTextFile<Digit>(@"./data/digits.csv", separatorChar: ',');
            var trainTestSplit = mlContext.Data.TrainTestSplit(data, testFraction: 0.2);
            var trainData = trainTestSplit.TrainSet;
            var testData = trainTestSplit.TestSet;

            // Define pipeline
            var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")
                .Append(mlContext.Transforms.Concatenate("Features", "PixelValues"))
                .Append(mlContext.Transforms.NormalizeMinMax("Features"))
                .Append(mlContext.Transforms.Conversion.MapKeyToValue("Label"))
                .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"))
                .Append(mlContext.Transforms.Conversion.ConvertToSingle("Score"))
                .Append(mlContext.Transforms.SelectColumns("Label", "PredictedLabel", "Score"));

            // Train model
            var estimator = mlContext.MulticlassClassification.Trainers.SdcaNonCalibrated()
                .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
            var model = pipeline.Append(estimator).Fit(trainData);

            // Evaluate model
            var predictions = model.Transform(testData);
            var metrics = mlContext.MulticlassClassification.Evaluate(predictions);

            Console.WriteLine($"Micro-accuracy: {metrics.MacroAccuracy}");
            Console.WriteLine($"Macro-accuracy: {metrics.MicroAccuracy}");
            Console.WriteLine($"Log-loss: {metrics.LogLoss}");

            // Load test image
            var image = Cv2.ImRead(@"./data/test_image.png", ImreadModes.GrayScale);

            // Threshold image
            var thresholded = new Mat();
            Cv2.Threshold(image, thresholded, 127, 255, ThresholdTypes.BinaryInv);

            // Find contours
            var contours = Cv2.FindContours(thresholded, RetrievalModes.External, ContourApproximationModes.ApproxSimple);

            // Sort contours from left to right
            var sortedContours = contours.OrderBy(c => Cv2.BoundingRect(c).X).ToList();

            // Create prediction engine
            var predictionEngine = mlContext.Model.CreatePredictionEngine<Digit, DigitPrediction>(model);

            // Recognize digits
            foreach (var contour in sortedContours)
            {
                // Extract digit image from contour
                var boundingRect = Cv2.BoundingRect(contour);
                var digitImage = image.SubMat(boundingRect);

                // Resize digit image to 28x28 pixels
                var resizedImage = new Mat();
                Cv2.Resize(digitImage, resizedImage, new Size(28, 28));

                // Convert digit image to pixel values
                var pixelValues = resizedImage.Reshape(1, 1).GetArray<byte>(0);

                // Create digit object and make prediction
                var digit = new Digit { PixelValues = pixelValues };
                var prediction = predictionEngine.Predict(d
            igit);

            // Print prediction
            Console.WriteLine($"Predicted digit: {prediction.PredictedLabel}");
        }
    }
}

// Define data classes
class Digit
{
    [LoadColumn(0)]
    public float Label { get; set; }

    [LoadColumn(1, 784)]
    [VectorType(784)]
    public float[] PixelValues { get; set; }
}

class DigitPrediction
{
    [ColumnName("PredictedLabel")]
    public float PredictedLabel { get; set; }

    [ColumnName("Score")]
    public float[] Score { get; set; }
}

该代码读取一个手写数字数据集（`digits.csv`），并使用ML.NET中的`SdcaNonCalibrated`分类器进行训练。训练后，代码将读取一个数字验证码图像（`test_image.png`），将其阈值化并找到包含每个数字的轮廓。然后，对于每个数字，代码将提取其图像并将其调整为28x28像素的大小。最后，代码将使用训练的分类器进行预测并输出识别出的数字。

请注意，此代码仅是示例，并且可能需要进行修改才能适应您的数据集和需求。