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).
