10.2. OCR of Hand-written Data using kNN¶
10.2.1. Goal¶
10.2.2. OCR of Hand-written Digits¶
Our goal is to build an application which can read the handwritten
digits. For this we need some train_data and test_data. OpenCV comes
with an image \(digits.png\) (in the folder
opencv/samples/python2/data/) which has 5000 handwritten digits (500
for each digit). Each digit is a 20x20 image. So our first step is to
split this image into 5000 different digits. For each digit, we flatten
it into a single row with 400 pixels. That is our feature set, ie
intensity values of all pixels. It is the simplest feature set we can
create. We use first 250 samples of each digit as train_data, and next
250 samples as test_data. So let’s prepare them first.
>>> import numpy as np
>>> import cv2 as cv
>>> from matplotlib import pyplot as plt
>>>
>>> img = cv.imread('/cvdata/digits.png')
>>> gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
>>> # Now we split the image to 5000 cells, each 20x20 size
>>> cells = [np.hsplit(row,100) for row in np.vsplit(gray,50)]
>>> # Make it into a Numpy array. It size will be (50,100,20,20)
>>> x = np.array(cells)
>>> # Now we prepare train_data and test_data.
>>> train = x[:,:50].reshape(-1,400).astype(np.float32) # Size = (2500,400)
>>> test = x[:,50:100].reshape(-1,400).astype(np.float32) # Size = (2500,400)
>>> # Create labels for train and test data
>>> k = np.arange(10)
>>> train_labels = np.repeat(k,250)[:,np.newaxis]
>>> test_labels = train_labels.copy()
>>> # Initiate kNN, train the data, then test it with test data for k=1
>>> knn = cv.ml.KNearest_create()
>>> knn.train(train, cv.ml.ROW_SAMPLE, train_labels)
>>> ret,result,neighbours,dist = knn.findNearest(test,k=5)
>>> # Now we check the accuracy of classification
>>> # For that, compare the result with test_labels and check which are wrong
>>> matches = result==test_labels
>>> correct = np.count_nonzero(matches)
>>> accuracy = correct*100.0/result.size
>>> print( accuracy )
91.76
So our basic OCR app is ready. This particular example gave me an accuracy of 91%. One option improve accuracy is to add more data for training, especially the wrong ones. So instead of finding this training data everytime I start application, I better save it, so that next time, I directly read this data from a file and start classification. You can do it with the help of some Numpy functions like np.savetxt, np.savez, np.load etc. Please check their docs for more details.
>>> # save the data
>>> np.savez('knn_data.npz',train=train, train_labels=train_labels)
>>> # Now load the data
>>> with np.load('knn_data.npz') as data:
>>> print( data.files )
>>> train = data['train']
>>> train_labels = data['train_labels']
['train', 'train_labels']
In my system, it takes around 4.4 MB of memory. Since we are using intensity values (uint8 data) as features, it would be better to convert the data to np.uint8 first and then save it. It takes only 1.1 MB in this case. Then while loading, you can convert back into float32.
10.2.3. OCR of English Alphabets¶
Next we will do the same for English alphabets, but there is a slight
change in data and feature set. Here, instead of images, OpenCV comes
with a data file, letter-recognition.data in opencv/samples/cpp/
folder. If you open it, you will see 20000 lines which may, on first
sight, look like garbage. Actually, in each row, first column is an
alphabet which is our label. Next 16 numbers following it are its
different features. These features are obtained from UCI Machine
Learning Repository. You can find
the details of these features in this
page.
There are 20000 samples available, so we take first 10000 data as training samples and remaining 10000 as test samples. We should change the alphabets to ascii characters because we can’t work with alphabets directly.
>>> import cv2 as cv
>>> import numpy as np
>>> # Load the data, converters convert the letter to a number
>>> data= np.loadtxt('/cvdata/letter-recognition.data',
>>> dtype= 'float32',
>>> delimiter = ',',
>>> converters= {0: lambda ch: ord(ch)-ord('A')})
>>> # split the data to two, 10000 each for train and test
>>> train, test = np.vsplit(data,2)
>>> # split trainData and testData to features and responses
>>> responses, trainData = np.hsplit(train,[1])
>>> labels, testData = np.hsplit(test,[1])
>>> # Initiate the kNN, classify, measure accuracy.
>>> knn = cv.ml.KNearest_create()
>>> knn.train(trainData, cv.ml.ROW_SAMPLE, responses)
>>> ret, result, neighbours, dist = knn.findNearest(testData, k=5)
>>> correct = np.count_nonzero(result == labels)
>>> accuracy = correct*100.0/10000
>>> print( accuracy )
93.06
It gives me an accuracy of 93.22%. Again, if you want to increase accuracy, you can iteratively add error data in each level.