現(xiàn)在很多場景需要使用的數(shù)字識別，比如銀行卡識別，以及車牌識別等，在AI領域有很多圖像識別算法，大多是居于opencv 或者谷歌開源的tesseract 識別.

由于公司業(yè)務需要，需要開發(fā)一個客戶端程序，同時需要在xp這種老古董的機子上運行，故研究了如下幾個數(shù)字識別方案，如果大家有更好的方案可以留言告知我，大家一起學習借鑒，不過需要支持XP系統(tǒng)，萬分感謝！

ocr 識別的不同選擇方案

?tesseract

?放棄：谷歌的開源tesseract ocr識別目前最新版本不支持xp系統(tǒng)

? ? ??云端ocr 識別接口(不適用)

?費用比較貴：?場景不同，我們的需求是可能毫秒級別就需要調(diào)用一次ocr 識別

? ? ??opencv

?概念:OpenCV是一個基于BSD許可（開源）發(fā)行的跨平臺計算機視覺庫，可以運行在Linux、Windows、Android和Mac OS操作系統(tǒng)上。它輕量級而且高效——由一系列 C 函數(shù)和少量 C++ 類構成，同時提供了Python、Ruby、MATLAB等語言的接口，實現(xiàn)了圖像處理和計算機視覺方面的很多通用算法。

以上幾種ocr 識別比較，最后選擇了opencv 的方式進行ocr 數(shù)字識別,下面講解通過ocr識別的基本流程和算法.

opencv 數(shù)字識別流程及算法解析

要通過opencv 進行數(shù)字識別離不開訓練庫的支持，需要對目標圖片進行大量的訓練，才能做到精準的識別出目標數(shù)字;下面我會分別講解圖片訓練的過程及識別的過程.

opencv 識別算法原理

1.比如下面一張圖片，需要從中識別出正確的數(shù)字，需要對圖片進行灰度、二值化、腐蝕、膨脹、尋找數(shù)字輪廓、切割等一系列操作.

原圖

灰度化圖

二值化圖

尋找輪廓

識別后的結果圖

以上就是簡單的圖片進行灰度化、二值化、尋找數(shù)字輪廓得到的識別結果(==這是基于我之前訓練過的數(shù)字模型下得到的識別結果==) 有些圖片比較賦值，比如存在背景斜杠等的圖片則需要一定的腐蝕或者膨脹等處理，才能尋找到正確的數(shù)字輪廓.

上面的說到我這里使用的是opencv 圖像處理庫進行的ocr 識別，那我這里簡單介紹下C# 怎么使用opencv 圖像處理看;

為了在xp上能夠運行 我這里通過nuget 包引用了 OpenCvSharp-AnyCPU 第三方庫,它使用的是opencv 2410 版本,你們?nèi)绻豢紤]xp系統(tǒng)的情況下開源使用最新的版本，最新版本支持了更多的識別算法.

右擊你的個人項目，選擇“管理Nuget程序包”。在包管理器頁面中，點擊“瀏覽”選項，然后在搜索框中鍵入“OpenCvSharp-AnyCPU”。選擇最頂端的正確項目，并在右側詳情頁中點擊“安裝”，等待安裝完成即可。

以上的核心代碼如下:

private void runSimpleOCR(string pathName){//構造opcvOcr 庫，這里的是我單獨對opencv 庫進行的一次封裝,加載訓練庫模板var opencvOcr = new OpencvOcr($"{path}Template\\Traindata.xml", opencvOcrConfig: new OCR.Model.OpencvOcrConfig(){ErodeLevel = 2.5,ThresholdType = OpenCvSharp.ThresholdType.Binary,ZoomLevel = 2,});var img = new Bitmap(this.txbFilaName.Text);var mat = img.ToMat();//核心識別方法var str = opencvOcr.GetText(mat, isDebug: true);this.labContent.Content = str;}

opencvOcr 的核心代碼如下

#region?屬性const double Thresh = 80;const double ThresholdMaxVal = 255;const int _minHeight = 35;bool _isDebug = false;CvKNearest _cvKNearest = null;OpencvOcrConfig _config = new OpencvOcrConfig() { ZoomLevel = 2, ErodeLevel = 3 };#endregion/// <summary>/// 構造函數(shù)/// </summary>/// <param name="path">訓練庫完整路徑</param>/// <param name="opencvOcrConfig">OCR相關配置信息</param>public OpencvOcr(string path, OpencvOcrConfig opencvOcrConfig = null){if (string.IsNullOrEmpty(path))throw new ArgumentNullException("path is not null");if (opencvOcrConfig != null)_config = opencvOcrConfig;this.LoadKnearest(path);}/// <summary>/// 加載Knn 訓練庫模型/// </summary>/// <param name="dataPathFile"></param>/// <returns></returns>private CvKNearest LoadKnearest(string dataPathFile){if (_cvKNearest == null){using (var fs = new FileStorage(dataPathFile, FileStorageMode.Read)){var samples = fs["samples"].ReadMat();var responses = fs["responses"].ReadMat();this._cvKNearest = new CvKNearest();this._cvKNearest.Train(samples, responses);}}return _cvKNearest;}/// <summary>/// OCR 識別,僅僅只能識別單行數(shù)字 /// </summary>/// <param name="kNearest">訓練庫</param>/// <param name="path">要識別的圖片路徑</param>public override string GetText(Mat src, bool isDebug = false){this._isDebug = isDebug;#region 圖片處理var respMat = MatProcessing(src, isDebug);if (respMat == null)return "";#endregion#region 查找輪廓var sortRect = FindContours(respMat.FindContoursMat);#endregionreturn GetText(sortRect, respMat.ResourcMat, respMat.RoiResultMat);}/// <summary>/// 查找輪廓/// </summary>/// <param name="src"></param>/// <returns></returns>private List<Rect> FindContours(Mat src){try{#region 查找輪廓Point[][] contours;HierarchyIndex[] hierarchyIndexes;Cv2.FindContours(src,out contours,out hierarchyIndexes,mode: OpenCvSharp.ContourRetrieval.External,method: OpenCvSharp.ContourChain.ApproxSimple);if (contours.Length == 0)throw new NotSupportedException("Couldn't find any object in the image.");#endregion#region 單行排序（目前僅僅支持單行文字,多行文字順序可能不對，按照x坐標進行排序）var sortRect = GetSortRect(contours, hierarchyIndexes);sortRect = sortRect.OrderBy(item => item.X).ToList();#endregionreturn sortRect;}catch { }return null;}/// <summary>/// 獲得切割后的數(shù)量列表/// </summary>/// <param name="contours"></param>/// <param name="hierarchyIndex"></param>/// <returns></returns>private List<Rect> GetSortRect(Point[][] contours, HierarchyIndex[] hierarchyIndex){var sortRect = new List<Rect>();var _contourIndex = 0;while ((_contourIndex >= 0)){var contour = contours[_contourIndex];var boundingRect = Cv2.BoundingRect(contour); //Find bounding rect for each contoursortRect.Add(boundingRect);_contourIndex = hierarchyIndex[_contourIndex].Next;}return sortRect;}/// <summary>/// 是否放大/// </summary>/// <param name="src"></param>/// <returns></returns>private bool IsZoom(Mat src){if (src.Height <= _minHeight)return true;return false;}private List<EnumMatAlgorithmType> GetAlgoritmList(Mat src){var result = new List<EnumMatAlgorithmType>();var algorithm = this._config.Algorithm;#region 自定義的算法try{if (algorithm.Contains("|")){result = algorithm.Split('|').ToList().Select(item => (EnumMatAlgorithmType)Convert.ToInt32(item)).ToList();if (!IsZoom(src))result.Remove(EnumMatAlgorithmType.Zoom);return result;}}catch { }#endregion#region 默認算法if (IsZoom(src)){result.Add(EnumMatAlgorithmType.Zoom);}if (this._config.ThresholdType == ThresholdType.Binary){//result.Add(EnumMatAlgorithmType.Blur);result.Add(EnumMatAlgorithmType.Gray);result.Add(EnumMatAlgorithmType.Thresh);if (this._config.DilateLevel > 0)result.Add(EnumMatAlgorithmType.Dilate);result.Add(EnumMatAlgorithmType.Erode);return result;}//result.Add(EnumMatAlgorithmType.Blur);result.Add(EnumMatAlgorithmType.Gray);result.Add(EnumMatAlgorithmType.Thresh);if (this._config.DilateLevel > 0)result.Add(EnumMatAlgorithmType.Dilate);result.Add(EnumMatAlgorithmType.Erode);return result;#endregion}/// <summary>/// 對查找的輪廓數(shù)據(jù)進行訓練模型匹配，這里使用的是KNN 匹配算法/// </summary>private string GetText(List<Rect> sortRect, Mat source, Mat roiSource){var response = "";try{if ((sortRect?.Count ?? 0) <= 0)return response;var contourIndex = 0;using (var dst = new Mat(source.Rows, source.Cols, MatType.CV_8UC3, Scalar.All(0))){sortRect.ForEach(boundingRect =>{try{#region 繪制矩形if (this._isDebug){Cv2.Rectangle(source, new Point(boundingRect.X, boundingRect.Y),new Point(boundingRect.X + boundingRect.Width, boundingRect.Y + boundingRect.Height),new Scalar(0, 0, 255), 1);Cv2.Rectangle(roiSource, new Point(boundingRect.X, boundingRect.Y),new Point(boundingRect.X + boundingRect.Width, boundingRect.Y + boundingRect.Height),new Scalar(0, 0, 255), 1);}#endregion#region 單個ROIvar roi = roiSource.GetROI(boundingRect); //Crop the imageroi = roi.Compress();var result = roi.ConvertFloat();#endregion#region KNN 匹配var results = new Mat();var neighborResponses = new Mat();var dists = new Mat();var detectedClass = (int)this._cvKNearest.FindNearest(result, 1, results, neighborResponses, dists);var resultText = detectedClass.ToString(CultureInfo.InvariantCulture);#endregion#region 匹配var isDraw = false;if (detectedClass >= 0){response += detectedClass.ToString();isDraw = true;}if (detectedClass == -1 && !response.Contains(".")){response += ".";resultText = ".";isDraw = true;}#endregion#region 繪制及輸出切割信息庫try{//if (this._isDebug)//{Write(contourIndex, detectedClass, roi);//}}catch { }if (this._isDebug && isDraw){Cv2.PutText(dst, resultText, new Point(boundingRect.X, boundingRect.Y + boundingRect.Height), 0, 1, new Scalar(0, 255, 0), 2);}#endregionresult?.Dispose();results?.Dispose();neighborResponses?.Dispose();dists?.Dispose();contourIndex++;}catch (Exception ex){TextHelper.Error("GetText ex", ex);}});#region 調(diào)試模式顯示過程source.IsDebugShow("Segmented Source", this._isDebug);dst.IsDebugShow("Detected", this._isDebug);dst.IsDebugWaitKey(this._isDebug);dst.IsDebugImWrite("dest.jpg", this._isDebug);#endregion}}catch{throw;}finally{source?.Dispose();roiSource?.Dispose();}return response;}/// <summary>/// 圖片處理算法/// </summary>/// <param name="src"></param>/// <param name="isDebug"></param>/// <returns></returns>public ImageProcessModel MatProcessing(Mat src, bool isDebug = false){src.IsDebugShow("原圖", isDebug);var list = GetAlgoritmList(src);var resultMat = new Mat();src.CopyTo(resultMat);var isZoom = IsZoom(src);list?.ForEach(item =>{switch (item){case EnumMatAlgorithmType.Dilate:resultMat = resultMat.ToDilate(Convert.ToInt32(this._config.DilateLevel));resultMat.IsDebugShow(EnumMatAlgorithmType.Dilate.GetDescription(), isDebug);break;case EnumMatAlgorithmType.Erode:var eroderLevel = isZoom ? this._config.ErodeLevel * this._config.ZoomLevel : this._config.ErodeLevel;resultMat = resultMat.ToErode(eroderLevel);resultMat.IsDebugShow(EnumMatAlgorithmType.Erode.GetDescription(), isDebug);break;case EnumMatAlgorithmType.Gray:resultMat = resultMat.ToGrey();resultMat.IsDebugShow(EnumMatAlgorithmType.Gray.GetDescription(), isDebug);break;case EnumMatAlgorithmType.Thresh:var thresholdValue = this._config.ThresholdValue <= 0 ? resultMat.GetMeanThreshold() : this._config.ThresholdValue;resultMat = resultMat.ToThreshold(thresholdValue, thresholdType: this._config.ThresholdType);resultMat.IsDebugShow(EnumMatAlgorithmType.Thresh.GetDescription(), isDebug);break;case EnumMatAlgorithmType.Zoom:resultMat = resultMat.ToZoom(this._config.ZoomLevel);src = resultMat;resultMat.IsDebugShow(EnumMatAlgorithmType.Zoom.GetDescription(), isDebug);break;case EnumMatAlgorithmType.Blur:resultMat = resultMat.ToBlur();src = resultMat;resultMat.IsDebugShow(EnumMatAlgorithmType.Blur.GetDescription(), isDebug);break;}});var oldThreshImage = new Mat();resultMat.CopyTo(oldThreshImage);return new ImageProcessModel(){ResourcMat = src,FindContoursMat = oldThreshImage,RoiResultMat = resultMat};}

opencv 圖片處理開放出去的配置對象實體如下：

public class OpencvOcrConfig{/// <summary>/// 放大程度級別 默認2/// </summary>public double ZoomLevel { set; get; }/// <summary>/// 腐蝕級別 默認2.5/// </summary>public double ErodeLevel { set; get; }/// <summary>/// 膨脹/// </summary>public double DilateLevel { set; get; }/// <summary>/// 閥值/// </summary>public double ThresholdValue { set; get; }/// <summary>/// 圖片處理算法,用逗號隔開/// </summary>public string Algorithm { set; get; }/// <summary>/// 二值化方式/// </summary>public ThresholdType ThresholdType { set; get; } = ThresholdType.BinaryInv;/// <summary>/// 通道模式/// </summary>public OcrChannelTypeEnums ChannelType { set; get; } = OcrChannelTypeEnums.BlackBox;}

opencv 圖片處理算法擴展方法如下：

public static partial class OpenCvExtensions{private const int Thresh = 200;private const int ThresholdMaxVal = 255;/// <summary>/// Bitmap Convert Mat/// </summary>/// <param name="bitmap"></param>/// <returns></returns>public static Mat ToMat(this System.Drawing.Bitmap bitmap){return OpenCvSharp.Extensions.BitmapConverter.ToMat(bitmap);}/// <summary>/// Bitmap Convert Mat/// </summary>/// <param name="bitmap"></param>/// <returns></returns>public static System.Drawing.Bitmap ToBitmap(this Mat mat){return OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);}public static bool MatIsEqual(this Mat mat1, Mat mat2){try{if (mat1.Empty() && mat2.Empty()){return true;}if (mat1.Cols != mat2.Cols || mat1.Rows != mat2.Rows || mat1.Dims() != mat2.Dims() ||mat1.Channels() != mat2.Channels()){return false;}if (mat1.Size() != mat2.Size() || mat1.Type() != mat2.Type()){return false;}var nrOfElements1 = mat1.Total() * mat1.ElemSize();if (nrOfElements1 != mat2.Total() * mat2.ElemSize())return false;return MatPixelEqual(mat1, mat2);}catch (Exception ex){TextHelper.Error("MatIsEqual 異常", ex);return true;}}/// <summary>/// 灰度/// </summary>/// <param name="mat"></param>/// <returns></returns>public static Mat ToGrey(this Mat mat){try{Mat grey = new Mat();Cv2.CvtColor(mat, grey, OpenCvSharp.ColorConversion.BgraToGray);return grey;}catch{return mat;}}/// <summary>/// 二值化/// </summary>/// <param name="data"></param>/// <returns></returns>public static Mat ToThreshold(this Mat data, double threshValue = 0, ThresholdType thresholdType = ThresholdType.BinaryInv){Mat threshold = new Mat();if (threshValue == 0)threshValue = Thresh;Cv2.Threshold(data, threshold, threshValue, ThresholdMaxVal, thresholdType);if (threshold.IsBinaryInv()){Cv2.Threshold(threshold, threshold, threshValue, ThresholdMaxVal, ThresholdType.BinaryInv);}return threshold;}/// <summary>/// 是否調(diào)試顯示/// </summary>/// <param name="src"></param>/// <param name="name"></param>/// <param name="isDebug"></param>public static void IsDebugShow(this Mat src, string name, bool isDebug = false){if (!isDebug)return;Cv2.ImShow(name, src);}public static void IsDebugWaitKey(this Mat src, bool isDebug = false){if (!isDebug)return;Cv2.WaitKey();}public static void IsDebugImWrite(this Mat src, string path, bool isDebug = false){if (!isDebug)return;try{Cv2.ImWrite(path, src);}catch { }}/// <summary>/// Mat 轉成另外一種存儲矩陣方式/// </summary>/// <param name="roi"></param>/// <returns></returns>public static Mat ConvertFloat(this Mat roi){var resizedImage = new Mat();var resizedImageFloat = new Mat();Cv2.Resize(roi, resizedImage, new Size(10, 10)); //resize to 10X10resizedImage.ConvertTo(resizedImageFloat, MatType.CV_32FC1); //convert to floatvar result = resizedImageFloat.Reshape(1, 1);return result;}/// <summary>/// 腐蝕/// </summary>/// <param name="mat"></param>/// <returns></returns>public static Mat ToErode(this Mat mat, double level){#region?自動會判斷是否需要腐蝕if (level < 1){return mat;}#endregionvar erode = new Mat();var copyMat = new Mat();mat.CopyTo(copyMat);Cv2.Erode(mat, erode, Cv2.GetStructuringElement(StructuringElementShape.Ellipse, new Size(level, level)));return erode;}/// <summary>/// 膨脹/// </summary>/// <param name="mat"></param>/// <returns></returns>public static Mat ToDilate(this Mat mat, int level){if (level <= 0)return mat;var dilate = new Mat();Cv2.Dilate(mat, dilate, Cv2.GetStructuringElement(StructuringElementShape.Ellipse, new Size(level, level)));return dilate;}/// <summary>/// mat 轉Roi/// </summary>/// <param name="image"></param>/// <param name="boundingRect"></param>/// <returns></returns>public static Mat GetROI(this Mat image, Rect boundingRect){try{return new Mat(image, boundingRect); //Crop the image}catch{}return null;}/// <summary>/// 獲取平均閥值/// </summary>/// <param name="mat"></param>/// <returns></returns>public static int GetMeanThreshold(this Mat mat){var width = mat.Width;var height = mat.Height;var m = mat.Reshape(1, width * height);return (int)m.Sum() / (width * height);}/// <summary>/// 獲得二值化閥值/// </summary>/// <param name="bitmap"></param>/// <returns></returns>public static int GetMeanThreshold(this System.Drawing.Bitmap bitmap){using (var mat = bitmap.ToMat())using (var grap = mat.ToGrey()){return grap.GetMeanThreshold();}}public static bool IsErode(this System.Drawing.Bitmap bitmap){using (var mat = bitmap.ToMat())using (var grap = mat.ToGrey()){var thresholdValue = grap.GetMeanThreshold();using (var threshold = grap.ToThreshold(thresholdValue, ThresholdType.BinaryInv)){return threshold.IsErode();}}}/// <summary>/// 放大/// </summary>/// <param name="img"></param>/// <param name="times"></param>/// <returns></returns>public static Mat ToZoom(this Mat img, double times){if (times <= 0)return img;var width = img.Width * times;var height = img.Height * times;img = img.Resize(new Size(width, height), 0, 0, Interpolation.NearestNeighbor);return img;}/// <summary>/// 均值濾波/// </summary>/// <param name="img"></param>/// <returns></returns>public static Mat ToBlur(this Mat img){return img.Blur(new Size(3, 3));}public static Mat Compress(this Mat img){var width = 28.0 * img.Width / img.Height;var fWidth = width / img.Width;var?fHeight?=?28.0?/?img.Height;img = img.Resize(new Size(width, 28), fWidth, fHeight, Interpolation.NearestNeighbor);return img;}public static bool MatPixelEqual(this Mat src, Mat are){var width = src.Width;var height = src.Height;var sum = width * height;for (int row = 0; row < height; row++){for (int col = 0; col < width; col++){byte p = src.At<byte>(row, col); //獲對應矩陣坐標的取像素byte pAre = are.At<byte>(row, col);if (p != pAre)return false;}}return true;}public static int GetSumPixelCount(this Mat threshold){var width = threshold.Width;var height = threshold.Height;var sum = width * height;var value = 0;for (int row = 0; row < height; row++){for (int col = 0; col < width; col++){byte p = threshold.At<byte>(row, col); //獲對應矩陣坐標的取像素value++;}}return value;}public static int GetPixelCount(this Mat threshold, System.Drawing.Color color){var width = threshold.Width;var height = threshold.Height;var sum = width * height;var value = 0;for (int row = 0; row < height; row++){for (int col = 0; col < width; col++){byte p = threshold.At<byte>(row, col); //獲對應矩陣坐標的取像素if (Convert.ToInt32(p) == color.R){value++;}}}return value;}/// <summary>/// 是否需要二值化反轉/// </summary>/// <param name="threshold"></param>/// <returns></returns>public static bool IsBinaryInv(this Mat threshold){var width = threshold.Width;var height = threshold.Height;var sum = Convert.ToDouble(width * height);var black = GetPixelCount(threshold, System.Drawing.Color.Black);return (Convert.ToDouble(black) / sum) < 0.5;}/// <summary>/// 是否需要腐蝕/// </summary>/// <param name="mat"></param>/// <returns></returns>public static bool IsErode(this Mat mat){var percent = mat.GetPercent();return percent >= 0.20;}/// <summary>/// 獲得白色像素占比/// </summary>/// <param name="threshold"></param>/// <returns></returns>public static double GetPercent(this Mat threshold){var width = threshold.Width;var height = threshold.Height;var sum = Convert.ToDouble(width * height);var white = GetPixelCount(threshold, System.Drawing.Color.White);return (Convert.ToDouble(white) / sum);}/// <summary>/// 根據(jù)模板查找目標圖片的在原圖標中的開始位置坐標/// </summary>/// <param name="source"></param>/// <param name="template"></param>/// <param name="matchTemplateMethod"></param>/// <returns></returns>public static Point FindTemplate(this Mat source, Mat template, MatchTemplateMethod matchTemplateMethod = MatchTemplateMethod.SqDiffNormed){if (source == null)return new OpenCvSharp.CPlusPlus.Point();var result = new Mat();Cv2.MatchTemplate(source, template, result, matchTemplateMethod);Cv2.MinMaxLoc(result, out OpenCvSharp.CPlusPlus.Point minVal, out OpenCvSharp.CPlusPlus.Point maxVal);var topLeft = new OpenCvSharp.CPlusPlus.Point();if (matchTemplateMethod == MatchTemplateMethod.SqDiff || matchTemplateMethod == MatchTemplateMethod.SqDiffNormed){topLeft = minVal;}else{topLeft = maxVal;}return topLeft;}}

以上代碼中開源對圖片進行輪廓切割，同時會生成切割后的圖片代碼如下

#region 繪制及輸出切割信息庫try{Write(contourIndex, detectedClass, roi);}catch { } #endregionprivate void Write(int contourIndex, int detectedClass, Mat roi) {Task.Factory.StartNew(() =>{try{var templatePath = $"{AppDomain.CurrentDomain.BaseDirectory}template";FileHelper.CreateDirectory(templatePath);var templatePathFile = $"{templatePath}/{contourIndex}_{detectedClass.ToString()}.png";Cv2.ImWrite(templatePathFile, roi);if (!roi.IsDisposed){roi.Dispose();}}catch {}}); }

切割后的圖片如下：

這里我已經(jīng)對數(shù)字進行切割好了，接下來就是需要對0-9 這些數(shù)字進行分類(建立文件夾進行數(shù)字歸類)，如下：

圖中的每一個分類都是我事先切割好的數(shù)字圖片,圖中有-1 和-2 這兩個特殊分類，-1 里面我是放的是“.”好的分類，用于訓練“.”的圖片，這樣就可以識別出小數(shù)點的數(shù)字支持. -2 這個分類主要是其他一些無關緊要的圖片，也就是不是數(shù)字和點的都歸為這一類中.

現(xiàn)在訓練庫分類已經(jīng)建立好了，接下來我們需要對這些分類數(shù)字進行歸一化處理，生成訓練模型. 代碼如下：

private void Button_Click_1(object sender, RoutedEventArgs e){var opencvOcr = new OpencvOcr($"{path}Template\\Traindata.xml", opencvOcrConfig: null);opencvOcr.Save($"{path}Template\\NumberWrite", outputPath: $"{path}Template\\Traindata.xml");MessageBox.Show("生成訓練庫成功");//var img = new Bitmap(this.txbFilaName.Text);//var str = opencvOcr.GetText(img.ToMat(), isDebug: true);//this.labContent.Content = str;}/// <summary>/// 保存訓練模型/// </summary>/// <param name="dataPath"></param>/// <param name="trainExt"></param>/// <param name="dataPathFile"></param>public void Save(string dataPath, string trainExt = "*.png", string outputPath = ""){if (string.IsNullOrEmpty(outputPath))throw new ArgumentNullException("save dataPath is not null");var trainingImages = this.ReadTrainingImages(dataPath, trainExt);var samples = GetSamples(trainingImages);var response = GetResponse(trainingImages);//寫入到訓練庫中using (var fs = new FileStorage(outputPath, FileStorageMode.WriteText)){fs.Write("samples", samples);fs.Write("responses", response);}}/// <summary>/// 根據(jù)目錄加載文件/// </summary>/// <param name="path"></param>/// <param name="ext"></param>/// <returns></returns>private IList<ImageInfo> ReadTrainingImages(string path, string ext){var images = new List<ImageInfo>();var imageId = 1;foreach (var dir in new DirectoryInfo(path).GetDirectories()){var groupId = int.Parse(dir.Name);foreach (var imageFile in dir.GetFiles(ext)){var srcMat = new Mat(imageFile.FullName, OpenCvSharp.LoadMode.GrayScale);var image = srcMat.ConvertFloat();if (image == null){continue;}images.Add(new ImageInfo{Image = image,ImageId = imageId++,ImageGroupId = groupId});}}return images;}/// <summary>/// Mat 轉成另外一種存儲矩陣方式/// </summary>/// <param name="roi"></param>/// <returns></returns>public static Mat ConvertFloat(this Mat roi){var resizedImage = new Mat();var resizedImageFloat = new Mat();Cv2.Resize(roi, resizedImage, new Size(10, 10)); //resize to 10X10resizedImage.ConvertTo(resizedImageFloat, MatType.CV_32FC1); //convert to floatvar result = resizedImageFloat.Reshape(1, 1);return result;}/// <summary>/// 獲取Samples/// </summary>/// <param name="trainingImages"></param>/// <returns></returns>private Mat GetSamples(IList<ImageInfo> trainingImages){var samples = new Mat();foreach (var trainingImage in trainingImages){samples.PushBack(trainingImage.Image);}return samples;}private Mat GetResponse(IList<ImageInfo> trainingImages){var labels = trainingImages.Select(x => x.ImageGroupId).ToArray();var responses = new Mat(labels.Length, 1, MatType.CV_32SC1, labels);var tmp = responses.Reshape(1, 1); //make continuousvar responseFloat = new Mat();tmp.ConvertTo(responseFloat, MatType.CV_32FC1); // Convert to floatreturn responses;}

到這里ocr 訓練模型以及建立好了，會在目錄中生成一個Traindata.xml 的訓練模型庫,我們來打開這個訓練模型庫文件探索它的神秘的容顏.

如果大家有更好的數(shù)字識別方案可以留言告知我，這樣可以更好的應用到實際場景中需求場景：

客戶端調(diào)用，不走api方式

必須要xp這種老爺機的支持

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