OpenCV-Python 中的简单数字识别 OCR

发布于 2025-01-08 14:43:33 字数 969 浏览 0 评论 0原文

我正在尝试在 OpenCV-Python (cv2) 中实现“数字识别 OCR”。它仅用于学习目的。我想学习 OpenCV 中的 KNearest 和 SVM 功能。

我有每个数字 100 个样本（即图像）。我想和他们一起训练。

OpenCV 示例附带了一个示例 letter_recog.py。但我仍然不知道如何使用它。我不明白示例、响应等是什么。此外，它首先加载一个 txt 文件，我一开始不明白。

后来稍微搜索了一下，我可以在cpp样本中找到letter_recognition.data。我用它并在 letter_recog.py 的模型中为 cv2.KNearest 编写了代码（仅用于测试）：

import numpy as np
import cv2

fn = 'letter-recognition.data'
a = np.loadtxt(fn, np.float32, delimiter=',', converters={ 0 : lambda ch : ord(ch)-ord('A') })
samples, responses = a[:,1:], a[:,0]

model = cv2.KNearest()
retval = model.train(samples,responses)
retval, results, neigh_resp, dists = model.find_nearest(samples, k = 10)
print results.ravel()

它给了我一个大小为 20000 的数组，我不明白它是什么。

问题：

1) letter_recognition.data 文件是什么？如何从我自己的数据集构建该文件？

2）results.reval()表示什么？

3）我们如何使用letter_recognition.data文件（KNearest或SVM）编写一个简单的数字识别工具？

原文

I am trying to implement a "Digit Recognition OCR" in OpenCV-Python (cv2). It is just for learning purposes. I would like to learn both KNearest and SVM features in OpenCV.

I have 100 samples (i.e. images) of each digit. I would like to train with them.

There is a sample letter_recog.py that comes with OpenCV sample. But I still couldn't figure out on how to use it. I don't understand what are the samples, responses etc. Also, it loads a txt file at first, which I didn't understand first.

Later on searching a little bit, I could find a letter_recognition.data in cpp samples. I used it and made a code for cv2.KNearest in the model of letter_recog.py (just for testing):

import numpy as np
import cv2

fn = 'letter-recognition.data'
a = np.loadtxt(fn, np.float32, delimiter=',', converters={ 0 : lambda ch : ord(ch)-ord('A') })
samples, responses = a[:,1:], a[:,0]

model = cv2.KNearest()
retval = model.train(samples,responses)
retval, results, neigh_resp, dists = model.find_nearest(samples, k = 10)
print results.ravel()

It gave me an array of size 20000, I don't understand what it is.

Questions:

1) What is letter_recognition.data file? How to build that file from my own data set?

2) What does results.reval() denote?

3) How we can write a simple digit recognition tool using letter_recognition.data file (either KNearest or SVM)?

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誰ツ都不明白 2025-01-15 14:43:33

好吧，我决定锻炼自己的问题来解决上述问题。我想要的是使用 OpenCV 中的 KNearest 或 SVM 功能来实现一个简单的 OCR。以下是我所做的以及如何做的。（它只是为了学习如何使用 KNearest 进行简单的 OCR 目的）。

1) 我的第一个问题是关于 OpenCV 示例附带的 letter_recognition.data 文件。我想知道该文件里面有什么。

它包含一封信以及该信的 16 个特征。

以及这个SOF 帮我找到了它。这16个特征在论文Letter中进行了解释使用 Holland 式自适应分类器进行识别。
（虽然最后我没有理解其中的一些功能）

2）因为我知道，如果不理解所有这些功能，很难做到那种方法。我尝试了一些其他论文，但对于初学者来说都有点困难。

所以我决定将所有像素值作为我的特征。（我并不担心准确性或性能，我只是希望它能够工作，至少以最低的准确性）

我将下图作为我的训练数据：

在此处输入图像描述

（我知道训练数据量较少。但是，由于所有字母的字体和大小相同，我决定尝试一下）。

为了准备训练数据，我在 OpenCV 中编写了一段小代码。它执行以下操作：

加载图像。
选择数字（显然是通过轮廓查找并对字母的面积和高度应用约束以避免错误检测）。
围绕一个字母绘制边框并等待手动按键。这次我们自己按下与框中字母相对应的数字键。
一旦按下相应的数字键，它就会将此框的大小调整为 10x10，并将所有 100 个像素值保存在一个数组（此处为样本）中，并将相应的手动输入的数字保存在另一个数组中（此处为响应）。
然后将两个数组保存在单独的 .txt 文件中。

在数字的手动分类结束时，训练数据（train.png）中的所有数字都由我们自己手动标记，图像如下所示：

在此处输入图像描述

下面是我用于上述目的的代码（当然，不是那么干净）：

import sys

import numpy as np
import cv2

im = cv2.imread('pitrain.png')
im3 = im.copy()

gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)
thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2)

#################      Now finding Contours         ###################

contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)

samples =  np.empty((0,100))
responses = []
keys = [i for i in range(48,58)]

for cnt in contours:
    if cv2.contourArea(cnt)>50:
        [x,y,w,h] = cv2.boundingRect(cnt)
        
        if  h>28:
            cv2.rectangle(im,(x,y),(x+w,y+h),(0,0,255),2)
            roi = thresh[y:y+h,x:x+w]
            roismall = cv2.resize(roi,(10,10))
            cv2.imshow('norm',im)
            key = cv2.waitKey(0)

            if key == 27:  # (escape to quit)
                sys.exit()
            elif key in keys:
                responses.append(int(chr(key)))
                sample = roismall.reshape((1,100))
                samples = np.append(samples,sample,0)

responses = np.array(responses,np.float32)
responses = responses.reshape((responses.size,1))
print "training complete"

np.savetxt('generalsamples.data',samples)
np.savetxt('generalresponses.data',responses)

现在我们进入训练和测试部分.

对于测试部分，我使用了下图具有我在训练阶段使用的相同类型的字母。

在此处输入图像描述

对于训练，我们执行以下操作：

加载 .txt< /code> 我们之前保存的文件
创建我们正在使用的分类器的实例（在本例中是 KNearest）
然后我们使用 KNearest.train 函数来训练数据

出于测试目的，我们执行以下操作：

的图像
用于测试过程像之前一样绘制图像并使用轮廓方法提取每个数字
为其绘制一个边界框，然后将其大小调整为 10x10，并将其像素值存储在数组中，如之前所做的那样。
然后我们使用 KNearest.find_nearest() 函数来查找与我们给出的最接近的项目。（如果幸运的话，它会识别正确的数字。）

我在下面的单个代码中包含了最后两个步骤（训练和测试）：

import cv2
import numpy as np

#######   training part    ############### 
samples = np.loadtxt('generalsamples.data',np.float32)
responses = np.loadtxt('generalresponses.data',np.float32)
responses = responses.reshape((responses.size,1))

model = cv2.KNearest()
model.train(samples,responses)

############################# testing part  #########################

im = cv2.imread('pi.png')
out = np.zeros(im.shape,np.uint8)
gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray,255,1,1,11,2)

contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)

for cnt in contours:
    if cv2.contourArea(cnt)>50:
        [x,y,w,h] = cv2.boundingRect(cnt)
        if  h>28:
            cv2.rectangle(im,(x,y),(x+w,y+h),(0,255,0),2)
            roi = thresh[y:y+h,x:x+w]
            roismall = cv2.resize(roi,(10,10))
            roismall = roismall.reshape((1,100))
            roismall = np.float32(roismall)
            retval, results, neigh_resp, dists = model.find_nearest(roismall, k = 1)
            string = str(int((results[0][0])))
            cv2.putText(out,string,(x,y+h),0,1,(0,255,0))

cv2.imshow('im',im)
cv2.imshow('out',out)
cv2.waitKey(0)

它起作用了，下面是我得到的结果：

在此处输入图像描述

这里它的准确率是 100%。我认为这是因为所有数字都是相同种类和相同大小的。

但无论如何，这对于初学者来说是一个好的开始（我希望如此）。

Well, I decided to workout myself on my question to solve the above problem. What I wanted is to implement a simple OCR using KNearest or SVM features in OpenCV. And below is what I did and how. (it is just for learning how to use KNearest for simple OCR purposes).

1) My first question was about letter_recognition.data file that comes with OpenCV samples. I wanted to know what is inside that file.

It contains a letter, along with 16 features of that letter.

And this SOF helped me to find it. These 16 features are explained in the paper Letter Recognition Using Holland-Style Adaptive Classifiers.
(Although I didn't understand some of the features at the end)

2) Since I knew, without understanding all those features, it is difficult to do that method. I tried some other papers, but all were a little difficult for a beginner.

So I just decided to take all the pixel values as my features. (I was not worried about accuracy or performance, I just wanted it to work, at least with the least accuracy)

I took the below image for my training data:

enter image description here

(I know the amount of training data is less. But, since all letters are of the same font and size, I decided to try on this).

To prepare the data for training, I made a small code in OpenCV. It does the following things:

It loads the image.
Selects the digits (obviously by contour finding and applying constraints on area and height of letters to avoid false detections).
Draws the bounding rectangle around one letter and wait for key press manually. This time we press the digit key ourselves corresponding to the letter in the box.
Once the corresponding digit key is pressed, it resizes this box to 10x10 and saves all 100 pixel values in an array (here, samples) and corresponding manually entered digit in another array(here, responses).
Then save both the arrays in separate .txt files.

At the end of the manual classification of digits, all the digits in the training data (train.png) are labeled manually by ourselves, image will look like below:

enter image description here

Below is the code I used for the above purpose (of course, not so clean):

import sys

import numpy as np
import cv2

im = cv2.imread('pitrain.png')
im3 = im.copy()

gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)
thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2)

#################      Now finding Contours         ###################

contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)

samples =  np.empty((0,100))
responses = []
keys = [i for i in range(48,58)]

for cnt in contours:
    if cv2.contourArea(cnt)>50:
        [x,y,w,h] = cv2.boundingRect(cnt)
        
        if  h>28:
            cv2.rectangle(im,(x,y),(x+w,y+h),(0,0,255),2)
            roi = thresh[y:y+h,x:x+w]
            roismall = cv2.resize(roi,(10,10))
            cv2.imshow('norm',im)
            key = cv2.waitKey(0)

            if key == 27:  # (escape to quit)
                sys.exit()
            elif key in keys:
                responses.append(int(chr(key)))
                sample = roismall.reshape((1,100))
                samples = np.append(samples,sample,0)

responses = np.array(responses,np.float32)
responses = responses.reshape((responses.size,1))
print "training complete"

np.savetxt('generalsamples.data',samples)
np.savetxt('generalresponses.data',responses)

Now we enter in to training and testing part.

For the testing part, I used the below image, which has the same type of letters I used for the training phase.

enter image description here

For training we do as follows:

Load the .txt files we already saved earlier
create an instance of the classifier we are using (it is KNearest in this case)
Then we use KNearest.train function to train the data

For testing purposes, we do as follows:

We load the image used for testing
process the image as earlier and extract each digit using contour methods
Draw a bounding box for it, then resize it to 10x10, and store its pixel values in an array as done earlier.
Then we use KNearest.find_nearest() function to find the nearest item to the one we gave. ( If lucky, it recognizes the correct digit.)

I included last two steps (training and testing) in single code below:

import cv2
import numpy as np

#######   training part    ############### 
samples = np.loadtxt('generalsamples.data',np.float32)
responses = np.loadtxt('generalresponses.data',np.float32)
responses = responses.reshape((responses.size,1))

model = cv2.KNearest()
model.train(samples,responses)

############################# testing part  #########################

im = cv2.imread('pi.png')
out = np.zeros(im.shape,np.uint8)
gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray,255,1,1,11,2)

contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)

for cnt in contours:
    if cv2.contourArea(cnt)>50:
        [x,y,w,h] = cv2.boundingRect(cnt)
        if  h>28:
            cv2.rectangle(im,(x,y),(x+w,y+h),(0,255,0),2)
            roi = thresh[y:y+h,x:x+w]
            roismall = cv2.resize(roi,(10,10))
            roismall = roismall.reshape((1,100))
            roismall = np.float32(roismall)
            retval, results, neigh_resp, dists = model.find_nearest(roismall, k = 1)
            string = str(int((results[0][0])))
            cv2.putText(out,string,(x,y+h),0,1,(0,255,0))

cv2.imshow('im',im)
cv2.imshow('out',out)
cv2.waitKey(0)

And it worked, below is the result I got:

enter image description here

Here it worked with 100% accuracy. I assume this is because all the digits are of the same kind and the same size.

But anyway, this is a good start to go for beginners (I hope so).

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寄意 2025-01-15 14:43:33

对C++代码感兴趣的可以参考下面的代码。
感谢Abid Rahman的精彩解释。

该过程与上面相同，但是轮廓查找仅使用第一层次轮廓，因此该算法仅使用每个数字的外轮廓。

创建样本和标签数据的代码

//Process image to extract contour
Mat thr,gray,con;
Mat src=imread("digit.png",1);
cvtColor(src,gray,CV_BGR2GRAY);
threshold(gray,thr,200,255,THRESH_BINARY_INV); //Threshold to find contour
thr.copyTo(con);

// Create sample and label data
vector< vector <Point> > contours; // Vector for storing contour
vector< Vec4i > hierarchy;
Mat sample;
Mat response_array;  
findContours( con, contours, hierarchy,CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE ); //Find contour

for( int i = 0; i< contours.size(); i=hierarchy[i][0] ) // iterate through first hierarchy level contours
{
    Rect r= boundingRect(contours[i]); //Find bounding rect for each contour
    rectangle(src,Point(r.x,r.y), Point(r.x+r.width,r.y+r.height), Scalar(0,0,255),2,8,0);
    Mat ROI = thr(r); //Crop the image
    Mat tmp1, tmp2;
    resize(ROI,tmp1, Size(10,10), 0,0,INTER_LINEAR ); //resize to 10X10
    tmp1.convertTo(tmp2,CV_32FC1); //convert to float
    sample.push_back(tmp2.reshape(1,1)); // Store  sample data
    imshow("src",src);
    int c=waitKey(0); // Read corresponding label for contour from keyoard
    c-=0x30;     // Convert ascii to intiger value
    response_array.push_back(c); // Store label to a mat
    rectangle(src,Point(r.x,r.y), Point(r.x+r.width,r.y+r.height), Scalar(0,255,0),2,8,0);    
}

// Store the data to file
Mat response,tmp;
tmp=response_array.reshape(1,1); //make continuous
tmp.convertTo(response,CV_32FC1); // Convert  to float

FileStorage Data("TrainingData.yml",FileStorage::WRITE); // Store the sample data in a file
Data << "data" << sample;
Data.release();

FileStorage Label("LabelData.yml",FileStorage::WRITE); // Store the label data in a file
Label << "label" << response;
Label.release();
cout<<"Training and Label data created successfully....!! "<<endl;

imshow("src",src);
waitKey();

用于训练和测试的代码

Mat thr,gray,con;
Mat src=imread("dig.png",1);
cvtColor(src,gray,CV_BGR2GRAY);
threshold(gray,thr,200,255,THRESH_BINARY_INV); // Threshold to create input
thr.copyTo(con);


// Read stored sample and label for training
Mat sample;
Mat response,tmp;
FileStorage Data("TrainingData.yml",FileStorage::READ); // Read traing data to a Mat
Data["data"] >> sample;
Data.release();

FileStorage Label("LabelData.yml",FileStorage::READ); // Read label data to a Mat
Label["label"] >> response;
Label.release();


KNearest knn;
knn.train(sample,response); // Train with sample and responses
cout<<"Training compleated.....!!"<<endl;

vector< vector <Point> > contours; // Vector for storing contour
vector< Vec4i > hierarchy;

//Create input sample by contour finding and cropping
findContours( con, contours, hierarchy,CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
Mat dst(src.rows,src.cols,CV_8UC3,Scalar::all(0));

for( int i = 0; i< contours.size(); i=hierarchy[i][0] ) // iterate through each contour for first hierarchy level .
{
    Rect r= boundingRect(contours[i]);
    Mat ROI = thr(r);
    Mat tmp1, tmp2;
    resize(ROI,tmp1, Size(10,10), 0,0,INTER_LINEAR );
    tmp1.convertTo(tmp2,CV_32FC1);
    float p=knn.find_nearest(tmp2.reshape(1,1), 1);
    char name[4];
    sprintf(name,"%d",(int)p);
    putText( dst,name,Point(r.x,r.y+r.height) ,0,1, Scalar(0, 255, 0), 2, 8 );
}

imshow("src",src);
imshow("dst",dst);
imwrite("dest.jpg",dst);
waitKey();

结果

在结果中，第一行中的点被检测为 8，我们还没有对点进行训练。另外，我正在考虑第一层次结构中的每个轮廓作为样本输入，用户可以通过计算面积来避免它。

For those who interested in C++ code can refer below code.
Thanks Abid Rahman for the nice explanation.

The procedure is same as above but, the contour finding uses only first hierarchy level contour, so that the algorithm uses only outer contour for each digit.

Code for creating sample and Label data

//Process image to extract contour
Mat thr,gray,con;
Mat src=imread("digit.png",1);
cvtColor(src,gray,CV_BGR2GRAY);
threshold(gray,thr,200,255,THRESH_BINARY_INV); //Threshold to find contour
thr.copyTo(con);

// Create sample and label data
vector< vector <Point> > contours; // Vector for storing contour
vector< Vec4i > hierarchy;
Mat sample;
Mat response_array;  
findContours( con, contours, hierarchy,CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE ); //Find contour

for( int i = 0; i< contours.size(); i=hierarchy[i][0] ) // iterate through first hierarchy level contours
{
    Rect r= boundingRect(contours[i]); //Find bounding rect for each contour
    rectangle(src,Point(r.x,r.y), Point(r.x+r.width,r.y+r.height), Scalar(0,0,255),2,8,0);
    Mat ROI = thr(r); //Crop the image
    Mat tmp1, tmp2;
    resize(ROI,tmp1, Size(10,10), 0,0,INTER_LINEAR ); //resize to 10X10
    tmp1.convertTo(tmp2,CV_32FC1); //convert to float
    sample.push_back(tmp2.reshape(1,1)); // Store  sample data
    imshow("src",src);
    int c=waitKey(0); // Read corresponding label for contour from keyoard
    c-=0x30;     // Convert ascii to intiger value
    response_array.push_back(c); // Store label to a mat
    rectangle(src,Point(r.x,r.y), Point(r.x+r.width,r.y+r.height), Scalar(0,255,0),2,8,0);    
}

// Store the data to file
Mat response,tmp;
tmp=response_array.reshape(1,1); //make continuous
tmp.convertTo(response,CV_32FC1); // Convert  to float

FileStorage Data("TrainingData.yml",FileStorage::WRITE); // Store the sample data in a file
Data << "data" << sample;
Data.release();

FileStorage Label("LabelData.yml",FileStorage::WRITE); // Store the label data in a file
Label << "label" << response;
Label.release();
cout<<"Training and Label data created successfully....!! "<<endl;

imshow("src",src);
waitKey();

Code for training and testing

Mat thr,gray,con;
Mat src=imread("dig.png",1);
cvtColor(src,gray,CV_BGR2GRAY);
threshold(gray,thr,200,255,THRESH_BINARY_INV); // Threshold to create input
thr.copyTo(con);


// Read stored sample and label for training
Mat sample;
Mat response,tmp;
FileStorage Data("TrainingData.yml",FileStorage::READ); // Read traing data to a Mat
Data["data"] >> sample;
Data.release();

FileStorage Label("LabelData.yml",FileStorage::READ); // Read label data to a Mat
Label["label"] >> response;
Label.release();


KNearest knn;
knn.train(sample,response); // Train with sample and responses
cout<<"Training compleated.....!!"<<endl;

vector< vector <Point> > contours; // Vector for storing contour
vector< Vec4i > hierarchy;

//Create input sample by contour finding and cropping
findContours( con, contours, hierarchy,CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
Mat dst(src.rows,src.cols,CV_8UC3,Scalar::all(0));

for( int i = 0; i< contours.size(); i=hierarchy[i][0] ) // iterate through each contour for first hierarchy level .
{
    Rect r= boundingRect(contours[i]);
    Mat ROI = thr(r);
    Mat tmp1, tmp2;
    resize(ROI,tmp1, Size(10,10), 0,0,INTER_LINEAR );
    tmp1.convertTo(tmp2,CV_32FC1);
    float p=knn.find_nearest(tmp2.reshape(1,1), 1);
    char name[4];
    sprintf(name,"%d",(int)p);
    putText( dst,name,Point(r.x,r.y+r.height) ,0,1, Scalar(0, 255, 0), 2, 8 );
}

imshow("src",src);
imshow("dst",dst);
imwrite("dest.jpg",dst);
waitKey();

Result

In the result the dot in the first line is detected as 8 and we haven’t trained for dot. Also I am considering every contour in first hierarchy level as the sample input, user can avoid it by computing the area.

Results

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[浮城] 2025-01-15 14:43:33

我在生成训练数据时遇到了一些问题，因为有时很难识别最后选择的字母，所以我将图像旋转了 1.5 度。现在按顺序选择每个字符，训练后测试仍然显示 100% 的准确率。这是代码：

import numpy as np
import cv2

def rotate_image(image, angle):
  image_center = tuple(np.array(image.shape[1::-1]) / 2)
  rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
  result = cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_LINEAR)
  return result

img = cv2.imread('training_image.png')
cv2.imshow('orig image', img)
whiteBorder = [255,255,255]
# extend the image border
image1 = cv2.copyMakeBorder(img, 80, 80, 80, 80, cv2.BORDER_CONSTANT, None, whiteBorder)
# rotate the image 1.5 degrees clockwise for ease of data entry
image_rot = rotate_image(image1, -1.5)
#crop_img = image_rot[y:y+h, x:x+w]
cropped = image_rot[70:350, 70:710]
cv2.imwrite('rotated.png', cropped)
cv2.imshow('rotated image', cropped)
cv2.waitKey(0)

对于示例数据，我对脚本进行了一些更改，如下所示：

import sys
import numpy as np
import cv2

def sort_contours(contours, x_axis_sort='LEFT_TO_RIGHT', y_axis_sort='TOP_TO_BOTTOM'):
    # initialize the reverse flag
    x_reverse = False
    y_reverse = False
    if x_axis_sort == 'RIGHT_TO_LEFT':
        x_reverse = True
    if y_axis_sort == 'BOTTOM_TO_TOP':
        y_reverse = True
    
    boundingBoxes = [cv2.boundingRect(c) for c in contours]
    
    # sorting on x-axis 
    sortedByX = zip(*sorted(zip(contours, boundingBoxes),
    key=lambda b:b[1][0], reverse=x_reverse))
    
    # sorting on y-axis 
    (contours, boundingBoxes) = zip(*sorted(zip(*sortedByX),
    key=lambda b:b[1][1], reverse=y_reverse))
    # return the list of sorted contours and bounding boxes
    return (contours, boundingBoxes)

im = cv2.imread('rotated.png')
im3 = im.copy()

gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)
thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2)

contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
contours, boundingBoxes = sort_contours(contours, x_axis_sort='LEFT_TO_RIGHT', y_axis_sort='TOP_TO_BOTTOM')

samples =  np.empty((0,100))
responses = []
keys = [i for i in range(48,58)]

for cnt in contours:
    if cv2.contourArea(cnt)>50:
        [x,y,w,h] = cv2.boundingRect(cnt)

        if  h>28 and h < 40:
            cv2.rectangle(im,(x,y),(x+w,y+h),(0,0,255),2)
            roi = thresh[y:y+h,x:x+w]
            roismall = cv2.resize(roi,(10,10))
            cv2.imshow('norm',im)
            key = cv2.waitKey(0)

            if key == 27:  # (escape to quit)
                sys.exit()
            elif key in keys:
                responses.append(int(chr(key)))
                sample = roismall.reshape((1,100))
                samples = np.append(samples,sample,0)

responses = np.array(responses,np.ubyte)
responses = responses.reshape((responses.size,1))
print("training complete")

np.savetxt('generalsamples.data',samples,fmt='%i')
np.savetxt('generalresponses.data',responses,fmt='%i')

I had some problems to generate the training data, because it was hard sometimes to identify the last selected letter, so I rotated the image 1.5 degrees. Now each character is selected in order and the test still shows a 100% accuracy rate after training. Here is the code:

import numpy as np
import cv2

def rotate_image(image, angle):
  image_center = tuple(np.array(image.shape[1::-1]) / 2)
  rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
  result = cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_LINEAR)
  return result

img = cv2.imread('training_image.png')
cv2.imshow('orig image', img)
whiteBorder = [255,255,255]
# extend the image border
image1 = cv2.copyMakeBorder(img, 80, 80, 80, 80, cv2.BORDER_CONSTANT, None, whiteBorder)
# rotate the image 1.5 degrees clockwise for ease of data entry
image_rot = rotate_image(image1, -1.5)
#crop_img = image_rot[y:y+h, x:x+w]
cropped = image_rot[70:350, 70:710]
cv2.imwrite('rotated.png', cropped)
cv2.imshow('rotated image', cropped)
cv2.waitKey(0)

For sample data, I made some changes to the script, like this:

import sys
import numpy as np
import cv2

def sort_contours(contours, x_axis_sort='LEFT_TO_RIGHT', y_axis_sort='TOP_TO_BOTTOM'):
    # initialize the reverse flag
    x_reverse = False
    y_reverse = False
    if x_axis_sort == 'RIGHT_TO_LEFT':
        x_reverse = True
    if y_axis_sort == 'BOTTOM_TO_TOP':
        y_reverse = True
    
    boundingBoxes = [cv2.boundingRect(c) for c in contours]
    
    # sorting on x-axis 
    sortedByX = zip(*sorted(zip(contours, boundingBoxes),
    key=lambda b:b[1][0], reverse=x_reverse))
    
    # sorting on y-axis 
    (contours, boundingBoxes) = zip(*sorted(zip(*sortedByX),
    key=lambda b:b[1][1], reverse=y_reverse))
    # return the list of sorted contours and bounding boxes
    return (contours, boundingBoxes)

im = cv2.imread('rotated.png')
im3 = im.copy()

gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)
thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2)

contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
contours, boundingBoxes = sort_contours(contours, x_axis_sort='LEFT_TO_RIGHT', y_axis_sort='TOP_TO_BOTTOM')

samples =  np.empty((0,100))
responses = []
keys = [i for i in range(48,58)]

for cnt in contours:
    if cv2.contourArea(cnt)>50:
        [x,y,w,h] = cv2.boundingRect(cnt)

        if  h>28 and h < 40:
            cv2.rectangle(im,(x,y),(x+w,y+h),(0,0,255),2)
            roi = thresh[y:y+h,x:x+w]
            roismall = cv2.resize(roi,(10,10))
            cv2.imshow('norm',im)
            key = cv2.waitKey(0)

            if key == 27:  # (escape to quit)
                sys.exit()
            elif key in keys:
                responses.append(int(chr(key)))
                sample = roismall.reshape((1,100))
                samples = np.append(samples,sample,0)

responses = np.array(responses,np.ubyte)
responses = responses.reshape((responses.size,1))
print("training complete")

np.savetxt('generalsamples.data',samples,fmt='%i')
np.savetxt('generalresponses.data',responses,fmt='%i')

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