使用KNN分类器进行数字识别前的预处理 [英] Pre-processing before digit recognition with KNN classifier

问题描述

现在我正在尝试使用OpenCV创建数字识别系统。 WEB中有很多文章和示例（甚至在上的表中我发现人们正在使用 deskewing ，噪声消除，模糊和像素移位技术。不幸的是，几乎所有文章的链接都被打破了。所以我决定自己做这样的预处理，因为我已经知道如何做了。

现在，我的算法如下：

1. 侵蚀图像（我认为我原来的数字太粗糙了）
   粗略的。）


2. 删除小轮廓。


3. 阈值和模糊图像。


4. 中心数字（而非移位）。

我认为在我的情况下不需要去歪斜，因为所有数字都是正常旋转的。而且我也不知道如何找到合适的旋转角度。
所以在此之后我得到了这些图片：

• 1


• 3 （以前不是 5 ）


• 5 （不是 8 ）


• 7 （利润！）

所以，这样的预处理对我有所帮助，但我需要更好的结果，因为在我看来这样应该没有问题地识别数字。

任何人都可以通过预处理给我任何建议吗？感谢您的帮助。

P.S。我可以上传我的源码（c ++）代码。

解决方案

我意识到我的错误 - 它与预处理无关全部（感谢 @DavidBrown 和 @John ）。我使用手写的数字数据集而不是打印（大写）。我没有在网上找到这样的数据库所以我决定自己创建它。我已将我的数据库上传到 Google云端硬盘。

以下是你如何使用它（训练和分类）：

  int digitSize = 16; 
 //返回特定目录中的文件列表
 static vector< string> getListFiles（const string& dirPath）
 {
 vector< string>结果; 
 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）; 
} 
返回结果; 
} 
 
 void DigitClassifier :: train（const string& imagesPath）
 {
 int num = 510; 
 int size = digitSize * digitSize; 
 Mat trainData = Mat（Size（size，num），CV_32FC1）; 
 Mat响应= Mat（Size（1，num），CV_32FC1）; 
 
 int counter = 0; 
 for（int i = 1; i< = 9; i ++）
 {
 char digit [2]; 
 sprintf（数字，％d /，i）; 
 string digitPath（digit）; 
 digitPath = imagesPath + digitPath; 
 vector< string> images = getListFiles（digitPath）; 
 for（int j = 0; j< images.size（）; 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< size; k ++）
 {
 trainData.at< float>（counter * size + k）= mat.at< float>（k）; 
} 
 responces.at< float>（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）; 
 
返回knn.find_nearest（tmp.reshape（1,1），5）; 
} 
  

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:

• is recognized as 7
• and are recognized as 5
• and are recognized as 1
• is recognized as 8

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.

解决方案

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<string> getListFiles(const string& dirPath)
{
    vector<string> 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<string> images = getListFiles(digitPath);
        for (int j=0; j<images.size(); 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<size; k++)
            {
                trainData.at<float>(counter*size+k) = mat.at<float>(k);
            }
            responces.at<float>(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);
}

这篇关于使用KNN分类器进行数字识别前的预处理的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！