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带有Aforge.net感知器神经网络的OCR回答错误

[英]OCR with perceptron neural network of Aforge.net answers wrong

I tried to make OCR by perceptrons with Aforge.Net in C#. 我试图用C#中的Aforge.Net用感知器制作OCR。 I learned my network with nine 30*30 pictures in binary. 我用9张30 * 30二进制二进制图片学习了我的网络。 But in the results, it recognizes everything as 'C'. 但是在结果中,它将所有内容识别为“ C”。 this is the code: 这是代码:

    private void button1_Click(object sender, EventArgs e)
    {
        AForge.Neuro.ActivationNetwork network = new AForge.Neuro.ActivationNetwork(new AForge.Neuro.BipolarSigmoidFunction(2), 900, 3);
        network.Randomize();
        AForge.Neuro.Learning.PerceptronLearning learning = new AForge.Neuro.Learning.PerceptronLearning(network);
        learning.LearningRate =1 ;
        double[][] input = new double[9][];
        for (int i = 0; i < 9; i++)
        {
            input[i] = new double[900];
        }
   //Reading A images
        for (int i = 1; i <= 3; i++)
        {
            Bitmap a = AForge.Imaging.Image.FromFile(path + "\\a" + i + ".bmp");
            for (int j = 0; j < 30; j++)
                for (int k = 0; k < 30; k++)
                {
                    if (a.GetPixel(j, k).ToKnownColor() == KnownColor.White)
                    {
                        input[i-1][j * 10 + k] = -1;
                    }
                    else
                        input[i-1][j * 10 + k] = 1;
                }
           // showImage(a);

        }
   //Reading B images
        for (int i = 1; i <= 3; i++)
        {
            Bitmap a = AForge.Imaging.Image.FromFile(path + "\\b" + i + ".bmp");
            for (int j = 0; j < 30; j++)
                for (int k = 0; k < 30; k++)
                {
                    if (a.GetPixel(j , k).ToKnownColor() == KnownColor.White)
                    {
                        input[i + 2][j * 10 + k] = -1;
                    }
                    else
                        input[i + 2][j * 10 + k] = 1;
                }
           // showImage(a);

        }
   //Reading C images
        for (int i = 1; i <= 3; i++)
        {
            Bitmap a = AForge.Imaging.Image.FromFile(path + "\\c" + i + ".bmp");
            for (int j = 0; j < 30; j++)
                for (int k = 0; k < 30; k++)
                {
                    if (a.GetPixel(j , k ).ToKnownColor() == KnownColor.White)
                    {
                        input[i + 5][j * 10 + k] = -1;
                    }
                    else
                        input[i + 5][j * 10 + k] = 1;
                }
           // showImage(a);

        }

        bool needToStop = false;
        int iteration = 0;
        while (!needToStop)
        {
            double error = learning.RunEpoch(input, new double[9][] { new double[3] { 1, -1, -1 },new double[3] { 1, -1, -1 },new double[3] { 1, -1, -1 },//A
                new double[3] { -1, 1, -1 },new double[3] { -1, 1, -1 },new double[3] { -1, 1, -1 },//B
                new double[3] { -1, -1, 1 },new double[3] { -1, -1, 1 },new double[3] { -1, -1, 1 } }//C
                    /*new double[9][]{ input[0],input[0],input[0],input[1],input[1],input[1],input[2],input[2],input[2]}*/
                );
            //learning.LearningRate -= learning.LearningRate / 1000;
            if (error == 0)
                break;
            else if (iteration < 1000)
                iteration++;
            else
                needToStop = true;
            System.Diagnostics.Debug.WriteLine("{0} {1}", error, iteration);
        }
        Bitmap b = AForge.Imaging.Image.FromFile(path + "\\b1.bmp");
    //Reading A Sample to test Netwok
        double[] sample = new double[900];
        for (int j = 0; j < 30; j++)
            for (int k = 0; k < 30; k++)
            {
                if (b.GetPixel(j , k ).ToKnownColor() == KnownColor.White)
                {
                    sample[j * 30 + k] = -1;
                }
                else
                    sample[j * 30 + k] = 1;
            }
        foreach (double d in network.Compute(sample))
            System.Diagnostics.Debug.WriteLine(d);//Output is Always C = {-1,-1,1}
    }

I really wanted to know why it answers wrong. 我真的很想知道为什么它回答错误。

While loading your initial 30x30 images into a double[900] array in the input structure you are using the following calculation: 在将初始的30x30图像加载到input结构中的double [900]数组中时,您使用的是以下计算:

for (int j = 0; j < 30; j++)
    for (int k = 0; k < 30; k++)
    {
        if (a.GetPixel(j, k).ToKnownColor() == KnownColor.White)
            input[i-1][j * 10 + k] = -1;
        else
            input[i-1][j * 10 + k] = 1;
    }

Your offset calculation is wrong here. 您的偏移量计算在这里是错误的。 You need to change j * 10 + k to j * 30 + k or you will get invalid results. 您需要将j * 10 + k更改为j * 30 + k否则您将得到无效的结果。 Later you use the correct offset calculation while loading the test image, which is why it's not being matched correctly against the corrupted samples. 稍后,您在加载测试图像时会使用正确的偏移量计算,这就是为什么它无法与损坏的样本正确匹配的原因。

You should write a method to load a bitmap into a double[900] array and call it for each image, instead of writing the same code multiple times. 您应该编写一种将位图加载到double[900]数组中并为每个图像调用它的方法,而不是多次编写相同的代码。 This helps to reduce problems like this, where different results are given by two pieces of code that should return the same result. 这有助于减少类似的问题,在该问题中,应返回相同结果的两段代码给出了不同的结果。

I tried your code. 我尝试了您的代码。 It helped me too and thanks a lot for that. 它也对我有帮助,对此非常感谢。 I could get your code working by doing some changes to getting bit array from the image. 通过对从图像中获取位数组进行一些更改,可以使您的代码正常工作。 Here's the method I used. 这是我使用的方法。

`
        private double[] GetImageData(Bitmap bmp)
        {
        double[] imageData = null;

        //Make the image grayscale
        Grayscale filter = new Grayscale(0.2125, 0.7154, 0.0721);
        bmp = filter.Apply(bmp);

        //Binarize the image
        AForge.Imaging.Filters.Threshold thFilter = new AForge.Imaging.Filters.Threshold(128);
        thFilter.ApplyInPlace(bmp);

        int height = bmp.Height;
        int width = bmp.Width;
        imageData = new double[height * width];
        int imagePointer = 0;
        System.Diagnostics.Debug.WriteLine("Height : " + height);
        System.Diagnostics.Debug.WriteLine("Width  : " + width);

        for (int i = 0; i < height; i++)
        {
            for (int j = 0; j < width; j++)
            {
                System.Diagnostics.Debug.Write(string.Format("({0}  , {1})     Color : {2}\n", i, j, bmp.GetPixel(i, j)));

                //Identify the black points of the image
                if (bmp.GetPixel(i, j) == Color.FromArgb(255, 0,  0, 0))
                {
                    imageData[imagePointer] = 1;
                }
                else
                {
                    imageData[imagePointer] = 0;
                }
                imagePointer++;
            }
            System.Diagnostics.Debug.WriteLine("");
        }
        System.Diagnostics.Debug.WriteLine("Bits  : " + imagePointer );
        return imageData;
    }`

Hope this will help. 希望这会有所帮助。 Thanks. 谢谢。

try this 尝试这个

double error = learning.RunEpoch(input, new double[9][] { new double[3] **{ 1, -1, -1 }**,new double[3] **{ -1, 1, -1 }**,new double[3] **{ -1, -1, 1 }**,//A
                new double[3] **{ 1, -1, -1 }**,new double[3] **{ -1, 1, -1 }**,new double[3] **{ -1, -1, 1 }**,//B
                new double[3] **{ 1, -1, -1 }**,new double[3] **{ -1, 1, -1 }**,new double[3] **{ -1, -1, 1 }** }//C

                );

or this way 或者这样

double[][] output = new double[patterns][];
            for (int j = 0; j < patterns; j++)
            {
                output[j] = new double[patterns];
                for (int i = 0; i < patterns; i++)
                {
                    if (i != j)
                    {
                        output[j][i] = -1;
                    }
                    else
                    {
                        output[j][i] = 1;
                    }
                }
            }


double error = learning.RunEpoch(input,output)

double[] netout = neuralNet.Compute(pattern);

 int maxIndex = 0;
            double max = netout[0];

            for (int i = 1; i < netout.Length; i++)
            {
                if (netout[i] > max)
                {
                    max = netout[i];
                    maxIndex = i;
                }
            }

if maxIndex=0 answer is A 如果maxIndex = 0答案是A

if maxIndex=1 answer is B 如果maxIndex = 1答案是B

if maxIndex=2 answer is C 如果maxIndex = 2答案是C

also I think that you must create matrix from images and use it as pattern, for example 20/20 or 15/15 or small, your 30/30 is big. 我还认为您必须根据图像创建矩阵并将其用作图案,例如20/20或15/15或更小,则30/30很大。

I use different way for get Image Scheme. 我使用不同的方式来获取图像方案。 I Divide image 20/20 and If one of pixels in rectangle is black (or another colour you want) save 1 in matrix, else 0. 我将图像除以20/20,如果矩形中的一个像素为黑色(或您想要的另一种颜色),则在矩阵中保存1,否则为0。

I make replace all pixels an after this I have only two colours, White and Black, I can manipulate with contour. 我将所有像素全部替换掉​​,之后只有白色和黑色两种颜色,可以按轮廓操作。

private void Cmd_ReplaceColors(ref WriteableBitmap Par_WriteableBitmap,int Par_Limit=180)
        {

            for (int y = 0; y < Par_WriteableBitmap.PixelHeight; y++)
            {
                for (int x = 0; x < Par_WriteableBitmap.PixelWidth; x++)
                {

                    Color color = Par_WriteableBitmap.GetPixel(x, y);

                    if (color == Colors.White)
                    {

                    }
                    else
                    {
                        if (color.R < Par_Limit)
                        {
                            Par_WriteableBitmap.SetPixel(x, y, Colors.Black);
                        }
                        else
                        {
                            Par_WriteableBitmap.SetPixel(x, y, Colors.White);
                        }

                    }

                }
            }

            Par_WriteableBitmap.Invalidate();
        }

1000 iterations in my opinion is small, better 100 000 :) 我认为1000次迭代很小,最好100 000次:)

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