如何将图像的单个字母旋转到正确的方向以获得最佳OCR？

在我之前的问题中,我改变了这个图像:

进入这个:

Tesseract OCR解释为:

1O351

在图像周围放置一个框架

实际上改善了OCR结果.

 1CB51

但是,我需要正确地将所有5个字符都放到OCR中,所以作为一个实验,我使用Paint.NET旋转并将每个字母对齐到正确的方向:

得出正确答案:

1CB52

我将如何在C#中执行此更正？

我已经对各种文本对齐算法做了一些研究,但它们都假设源图像中存在文本行,可以从中导出旋转角度的行,但是已经包含了正确的间距和方向关系.这些信.

您可以使用以下代码项目文章中的代码来分割每个单独的字符.然而,当试图单独去除这些角色时,你获得的任何结果都不会很好,因为没有太多的信息可以解决.

我尝试使用AForge.NETs HoughLineTransformation级,我的角度范围为80-90度.所以我尝试使用以下代码来纠正它们:

private static Bitmap DeskewImageByIndividualChars(Bitmap targetBitmap)
{
    IDictionary characters = new CCL().Process(targetBitmap);

    using (Graphics g = Graphics.FromImage(targetBitmap))
    {
        foreach (var character in characters)
        {
            double angle;

            BitmapData bitmapData = character.Value.LockBits(new Rectangle(Point.Empty, character.Value.Size), ImageLockMode.ReadWrite, PixelFormat.Format8bppIndexed);
            try
            {
                HoughLineTransformation hlt = new HoughLineTransformation();
                hlt.ProcessImage(bitmapData);

                angle = hlt.GetLinesByRelativeIntensity(0.5).Average(l => l.Theta);
            }
            finally
            {
                character.Value.UnlockBits(bitmapData);
            }

            using (Bitmap bitmap = RotateImage(character.Value, 90 - angle, Color.White))
            {
                g.DrawImage(bitmap, character.Key.Location);
            }
        }
    }

    return targetBitmap;
}

用这个RotateImage方法取自这里.但是,结果似乎并不是最好的.也许你可以尝试让它们变得更好.

以下是代码项目文章中的代码供您参考.因此,它的行为比较安全,比如增加我已经给它的一些变化try-finally周围LockBits,并采用正确的对象的配置using语句等.

using System.Collections.Generic;
using System.Drawing;
using System.Drawing.Imaging;
using System.Linq;

namespace ConnectedComponentLabeling
{
    public class CCL
    {
        private Bitmap _input;
        private int[,] _board;

        public IDictionary Process(Bitmap input)
        {
            _input = input;
            _board = new int[_input.Width, _input.Height];

            Dictionary> patterns = Find();
            var images = new Dictionary();

            foreach (KeyValuePair> pattern in patterns)
            {
                using (Bitmap bmp = CreateBitmap(pattern.Value))
                {
                    images.Add(GetBounds(pattern.Value), (Bitmap)bmp.Clone());
                }
            }

            return images;
        }

        protected virtual bool CheckIsBackGround(Pixel currentPixel)
        {
            return currentPixel.color.A == 255 && currentPixel.color.R == 255 && currentPixel.color.G == 255 && currentPixel.color.B == 255;
        }

        private unsafe Dictionary> Find()
        {
            int labelCount = 1;
            var allLabels = new Dictionary();

            BitmapData imageData = _input.LockBits(new Rectangle(0, 0, _input.Width, _input.Height), ImageLockMode.ReadOnly, PixelFormat.Format24bppRgb);
            try
            {
                int bytesPerPixel = 3;

                byte* scan0 = (byte*)imageData.Scan0.ToPointer();
                int stride = imageData.Stride;

                for (int i = 0; i < _input.Height; i++)
                {
                    byte* row = scan0 + (i * stride);

                    for (int j = 0; j < _input.Width; j++)
                    {
                        int bIndex = j * bytesPerPixel;
                        int gIndex = bIndex + 1;
                        int rIndex = bIndex + 2;

                        byte pixelR = row[rIndex];
                        byte pixelG = row[gIndex];
                        byte pixelB = row[bIndex];

                        Pixel currentPixel = new Pixel(new Point(j, i), Color.FromArgb(pixelR, pixelG, pixelB));

                        if (CheckIsBackGround(currentPixel))
                        {
                            continue;
                        }

                        IEnumerable neighboringLabels = GetNeighboringLabels(currentPixel);
                        int currentLabel;

                        if (!neighboringLabels.Any())
                        {
                            currentLabel = labelCount;
                            allLabels.Add(currentLabel, new Label(currentLabel));
                            labelCount++;
                        }
                        else
                        {
                            currentLabel = neighboringLabels.Min(n => allLabels[n].GetRoot().Name);
                            Label root = allLabels[currentLabel].GetRoot();

                            foreach (var neighbor in neighboringLabels)
                            {
                                if (root.Name != allLabels[neighbor].GetRoot().Name)
                                {
                                    allLabels[neighbor].Join(allLabels[currentLabel]);
                                }
                            }
                        }

                        _board[j, i] = currentLabel;
                    }
                }
            }
            finally
            {
                _input.UnlockBits(imageData);
            }

            Dictionary> patterns = AggregatePatterns(allLabels);

            patterns = RemoveIntrusions(patterns, _input.Width, _input.Height);

            return patterns;
        }

        private Dictionary> RemoveIntrusions(Dictionary> patterns, int width, int height)
        {
            var patternsCleaned = new Dictionary>();

            foreach (var pattern in patterns)
            {
                bool bad = false;
                foreach (Pixel item in pattern.Value)
                {
                    //Horiz
                    if (item.Position.X == 0)
                        bad = true;

                    else if (item.Position.Y == width - 1)
                        bad = true;

                    //Vert
                    else if (item.Position.Y == 0)
                        bad = true;

                    else if (item.Position.Y == height - 1)
                        bad = true;
                }

                if (!bad)
                    patternsCleaned.Add(pattern.Key, pattern.Value);

            }

            return patternsCleaned;
        }

        private IEnumerable GetNeighboringLabels(Pixel pix)
        {
            var neighboringLabels = new List();

            for (int i = pix.Position.Y - 1; i <= pix.Position.Y + 2 && i < _input.Height - 1; i++)
            {
                for (int j = pix.Position.X - 1; j <= pix.Position.X + 2 && j < _input.Width - 1; j++)
                {
                    if (i > -1 && j > -1 && _board[j, i] != 0)
                    {
                        neighboringLabels.Add(_board[j, i]);
                    }
                }
            }

            return neighboringLabels;
        }

        private Dictionary> AggregatePatterns(Dictionary allLabels)
        {
            var patterns = new Dictionary>();

            for (int i = 0; i < _input.Height; i++)
            {
                for (int j = 0; j < _input.Width; j++)
                {
                    int patternNumber = _board[j, i];
                    if (patternNumber != 0)
                    {
                        patternNumber = allLabels[patternNumber].GetRoot().Name;

                        if (!patterns.ContainsKey(patternNumber))
                        {
                            patterns[patternNumber] = new List();
                        }

                        patterns[patternNumber].Add(new Pixel(new Point(j, i), Color.Black));
                    }
                }
            }

            return patterns;
        }

        private unsafe Bitmap CreateBitmap(List pattern)
        {
            int minX = pattern.Min(p => p.Position.X);
            int maxX = pattern.Max(p => p.Position.X);

            int minY = pattern.Min(p => p.Position.Y);
            int maxY = pattern.Max(p => p.Position.Y);

            int width = maxX + 1 - minX;
            int height = maxY + 1 - minY;

            Bitmap bmp = DrawFilledRectangle(width, height);

            BitmapData imageData = bmp.LockBits(new Rectangle(0, 0, bmp.Width, bmp.Height), ImageLockMode.ReadWrite, PixelFormat.Format24bppRgb);
            try
            {
                byte* scan0 = (byte*)imageData.Scan0.ToPointer();
                int stride = imageData.Stride;

                foreach (Pixel pix in pattern)
                {
                    scan0[((pix.Position.X - minX) * 3) + (pix.Position.Y - minY) * stride] = pix.color.B;
                    scan0[((pix.Position.X - minX) * 3) + (pix.Position.Y - minY) * stride + 1] = pix.color.G;
                    scan0[((pix.Position.X - minX) * 3) + (pix.Position.Y - minY) * stride + 2] = pix.color.R;
                }
            }
            finally
            {
                bmp.UnlockBits(imageData);
            }

            return bmp;
        }

        private Bitmap DrawFilledRectangle(int x, int y)
        {
            Bitmap bmp = new Bitmap(x, y);
            using (Graphics graph = Graphics.FromImage(bmp))
            {
                Rectangle ImageSize = new Rectangle(0, 0, x, y);
                graph.FillRectangle(Brushes.White, ImageSize);
            }

            return bmp;
        }

        private Rectangle GetBounds(List pattern)
        {
            var points = pattern.Select(x => x.Position);

            var x_query = points.Select(p => p.X);
            int xmin = x_query.Min();
            int xmax = x_query.Max();

            var y_query = points.Select(p => p.Y);
            int ymin = y_query.Min();
            int ymax = y_query.Max();

            return new Rectangle(xmin, ymin, xmax - xmin, ymax - ymin);
        }
    }
}

使用上面的代码我得到以下输入/输出:

正如你所看到的那样B旋转得很好但其他的并不是那么好.

尝试去除个别字符的另一种方法是使用上面的分割例程找到位置.然后将每个单独的角色分别传递到您的识别引擎,看看这是否会改善您的结果.

我使用以下方法使用类中的Listfrom 来查找字符的角度CCL.它的工作原理是找到"左下"和"右下"之间的角度.我没有测试过,如果角色是相反的,那么它是否有效.

private double GetAngle(List pattern)
{
    var pixels = pattern.Select(p => p.Position).ToArray();

    Point bottomLeft = pixels.OrderByDescending(p => p.Y).ThenBy(p => p.X).First();
    Point rightBottom = pixels.OrderByDescending(p => p.X).ThenByDescending(p => p.Y).First();

    int xDiff = rightBottom.X - bottomLeft.X;
    int yDiff = rightBottom.Y - bottomLeft.Y;

    double angle = Math.Atan2(yDiff, xDiff) * 180 / Math.PI;

    return -angle;
}

请注意我的绘图代码有点破,所以这就是为什么5在右边切断但这段代码产生以下输出:

请注意,由于它们的曲率,旋转B和5旋转的程度超出了您的预期.

通过从左右边缘获得角度,然后选择最佳角度,使用以下代码,旋转似乎更好.注意我只用需要顺时针旋转的字母进行了测试,所以如果它们需要采用相反的方式,它可能效果不好.

这也是"象限"像素,以便从它自己的象限中选择每个像素,而不是得到两个太近的像素.

选择最佳角度的想法是,如果它们相似,在彼此相差1.5度的时刻,但可以很容易地更新,平均它们.另外,我们选择最接近零的那个.

private double GetAngle(List pattern, Rectangle bounds)
{
    int halfWidth = bounds.X + (bounds.Width / 2);
    int halfHeight = bounds.Y + (bounds.Height / 2);

    double leftEdgeAngle = GetAngleLeftEdge(pattern, halfWidth, halfHeight);
    double rightEdgeAngle = GetAngleRightEdge(pattern, halfWidth, halfHeight);

    if (Math.Abs(leftEdgeAngle - rightEdgeAngle) <= 1.5)
    {
        return (leftEdgeAngle + rightEdgeAngle) / 2d;
    }

    if (Math.Abs(leftEdgeAngle) > Math.Abs(rightEdgeAngle))
    {
        return rightEdgeAngle;
    }
    else
    {
        return leftEdgeAngle;
    }
}

private double GetAngleLeftEdge(List pattern, double halfWidth, double halfHeight)
{
    var topLeftPixels = pattern.Select(p => p.Position).Where(p => p.Y < halfHeight && p.X < halfWidth).ToArray();
    var bottomLeftPixels = pattern.Select(p => p.Position).Where(p => p.Y > halfHeight && p.X < halfWidth).ToArray();

    Point topLeft = topLeftPixels.OrderBy(p => p.X).ThenBy(p => p.Y).First();
    Point bottomLeft = bottomLeftPixels.OrderByDescending(p => p.Y).ThenBy(p => p.X).First();

    int xDiff = bottomLeft.X - topLeft.X;
    int yDiff = bottomLeft.Y - topLeft.Y;

    double angle = Math.Atan2(yDiff, xDiff) * 180 / Math.PI;

    return 90 - angle;
}

private double GetAngleRightEdge(List pattern, double halfWidth, double halfHeight)
{
    var topRightPixels = pattern.Select(p => p.Position).Where(p => p.Y < halfHeight && p.X > halfWidth).ToArray();
    var bottomRightPixels = pattern.Select(p => p.Position).Where(p => p.Y > halfHeight && p.X > halfWidth).ToArray();

    Point topRight = topRightPixels.OrderBy(p => p.Y).ThenByDescending(p => p.X).First();
    Point bottomRight = bottomRightPixels.OrderByDescending(p => p.X).ThenByDescending(p => p.Y).First();

    int xDiff = bottomRight.X - topRight.X;
    int yDiff = bottomRight.Y - topRight.Y;

    double angle = Math.Atan2(xDiff, yDiff) * 180 / Math.PI;

    return Math.Abs(angle);
}

现在生成以下输出,我的绘图代码再次略有破坏.请注意,C看起来没有很好地纠正,但仔细观察它只是导致这种情况发生的形状.

我改进了绘图代码,并尝试将字符放到相同的基线上:

private static Bitmap DeskewImageByIndividualChars(Bitmap bitmap)
{
    IDictionary> characters = new CCL().Process(bitmap);

    Bitmap deskewedBitmap = new Bitmap(bitmap.Width, bitmap.Height, bitmap.PixelFormat);
    deskewedBitmap.SetResolution(bitmap.HorizontalResolution, bitmap.VerticalResolution);

    using (Graphics g = Graphics.FromImage(deskewedBitmap))
    {
        g.FillRectangle(Brushes.White, new Rectangle(Point.Empty, deskewedBitmap.Size));

        int baseLine = characters.Max(c => c.Key.Bottom);
        foreach (var character in characters)
        {
            int y = character.Key.Y;
            if (character.Key.Bottom != baseLine)
            {
                y += (baseLine - character.Key.Bottom - 1);
            }

            using (Bitmap characterBitmap = RotateImage(character.Value.Item1, character.Value.Item2, Color.White))
            {
                g.DrawImage(characterBitmap, new Point(character.Key.X, y));
            }
        }
    }

    return deskewedBitmap;
}

然后,这将产生以下输出.请注意,每个角色不在完全相同的基线上,因为需要使用预旋转底部来完成它.需要使用后期轮换的基线来改进代码.在做基线之前对图像进行阈值处理也会有所帮助.

另一个改进是计算Right每个旋转的字符位置,因此在绘制下一个旋转的字符时,它不会与前一个和切割的位重叠.因为你可以在输出中看到2它略微切入5.

输出现在非常类似于OP中手动创建的输出.