c++ - Pre-processing before digit recognition with KNN classifier

Right now I'm trying to create digit recognition system using OpenCV. There are many articles and examples in WEB (and even on StackOverflow). I decided to use KNN classifier because this solution is the most popular in WEB. I found a database of handwritten digits with a training set of 60k examples and with error rate less than 5%.

I used this tutorial as an example of how to work with this database using OpenCV. I'm using exactly same technique and on test data (t10k-images.idx3-ubyte) I've got 4% error rate. But when I try to classify my own digits I've got much bigger error. For example:

And so on (I can upload all images if it's needed).

As you can see all digits have good quality and are easily-recognizable for human.

So I decided to do some pre-processing before classifying. From the table on MNIST database site I found that people are using deskewing, noise removal, blurring and pixel shift techniques. Unfortunately almost all links to the articles are broken. So I decided to do such pre-processing by myself, because I already know how to do that.

Right now, my algorithm is the following:

1. Erode image (I think that my original digits are too
   rough).


2. Remove small contours.


3. Threshold and blur image.



4. Center digit (instead of shifting).

I think that deskewing is not needed in my situation because all digits are normally rotated. And also I have no idea how to find a right rotation angle.
So after this I've got these images:

• is also 1


• is 3 (not 5 as it used to be)


• is 5 (not 8)



• is 7 (profit!)

So, such pre-processing helped me a bit, but I need better results, because in my opinion such digits should be recognized without problems.

Can anyone give me any advice with pre-processing? Thanks for any help.

P.S. I can upload my source (c++) code.

Answer

I realized my mistake - it wasn't connected with pre-processing at all (thanks to @DavidBrown and @John). I used handwritten dataset of digits instead of printed (capitalized). I didn't find such database in the web so I decided to create it by myself. I have uploaded my database to the Google Drive.

And here's how you can use it (train and classify):

int digitSize = 16;
//returns list of files in specific directory
static vector getListFiles(const string& dirPath)
{
    vector result;
    DIR *dir;
    struct dirent *ent;

    if ((dir = opendir(dirPath.c_str())) != NULL)
    {
        while ((ent = readdir (dir)) != NULL)
        {
            if (strcmp(ent->d_name, ".") != 0 && strcmp(ent->d_name, "..") != 0 )
            {
                result.push_back(ent->d_name);
            }
        }
        closedir(dir);

    }
    return result;
}

void DigitClassifier::train(const string& imagesPath)
{
    int num = 510;
    int size = digitSize * digitSize;
    Mat trainData = Mat(Size(size, num), CV_32FC1);
    Mat responces = Mat(Size(1, num), CV_32FC1);


    int counter = 0;
    for (int i=1; i<=9; i++)
    {
        char digit[2];
        sprintf(digit, "%d/", i);
        string digitPath(digit);
        digitPath = imagesPath + digitPath;
        vector images = getListFiles(digitPath);
        for (int j=0; j
        {
            Mat mat = imread(digitPath+images[j], 0);
            resize(mat, mat, Size(digitSize, digitSize));
            mat.convertTo(mat, CV_32FC1);
            mat = mat.reshape(1,1);
            for (int k=0; k            {
                trainData.at(counter*size+k) = mat.at(k);
            }
            responces.at(counter) = i;

            counter++;
        }
    }
    knn.train(trainData, responces);
}

int DigitClassifier::classify(const Mat& img) const
{
    Mat tmp = img.clone();


    resize(tmp, tmp, Size(digitSize, digitSize));

    tmp.convertTo(tmp, CV_32FC1);

    return knn.find_nearest(tmp.reshape(1, 1), 5);
}