using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Linq;

namespace OpenCvSharp_DigitRecognition
{
    class Program
    {
        static void Main(string[] args)
        {
            // Load image and convert to grayscale
            Mat image = Cv2.ImRead("test_image.png");
            Mat gray = new Mat();
            Cv2.CvtColor(image, gray, ColorConversionCodes.BGR2GRAY);

            // Apply thresholding
            Mat thresh = new Mat();
            Cv2.Threshold(gray, thresh, 0, 255, ThresholdTypes.BinaryInv | ThresholdTypes.Otsu);

            // Find contours
            Point[][] contours;
            HierarchyIndex[] hierarchy;
            Cv2.FindContours(thresh, out contours, out hierarchy, RetrievalModes.List, ContourApproximationModes.ApproxSimple);

            // Sort contours by x-coordinate
            List<Point[]> sortedContours = contours.OrderBy(c => Cv2.BoundingRect(c).X).ToList();

            // Load SVM model
            using (var model = CvSVM.Load("svm_model.xml"))
            {
                // Loop over contours and recognize digits
                foreach (var contour in sortedContours)
                {
                    // Extract digit image
                    Rect rect = Cv2.BoundingRect(contour);
                    Mat digit = new Mat(thresh, rect);

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

                    // Flatten image and convert to float array
                    float[] data = new float[28 * 28];
                    int index = 0;
                    for (int i = 0; i < resized.Rows; i++)
                    {
                        for (int j = 0; j < resized.Cols; j++)
                        {
                            data[index] = resized.At<byte>(i, j) / 255.0f;
                            index++;
                        }
                    }

                    // Predict digit using SVM model
                    Mat sampleMat = new Mat(1, 28 * 28, MatType.CV_32FC1, data);
                    float prediction = model.Predict(sampleMat);

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

该代码读取一个数字验证码图像（test_image.png），将其转换为灰度图像并进行阈值化。然后，代码将找到包含每个数字的轮廓，并根据它们的x坐标对轮廓进行排序。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SVM模型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。