I am experimenting with using OpenCV via the Python 2.7 interface to implement a machine learning-based OCR application to parse text out of an image file. I am using this tutorial (I've reposted the code below for convenience). I am completely new to machine learning, and relatively new to OpenCV.

OCR of Hand-written Digits:

import numpy as np import cv2 from matplotlib import pyplot as plt img = cv2.imread('digits.png') gray = cv2.cvtColor(img,cv2.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 = cv2.KNearest() knn.train(train,train_labels) ret,result,neighbours,dist = knn.find_nearest(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 # 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']

OCR of English Alphabets:

import cv2 import numpy as np import matplotlib.pyplot as plt # Load the data, converters convert the letter to a number data= np.loadtxt('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 = cv2.KNearest() knn.train(trainData, responses) ret, result, neighbours, dist = knn.find_nearest(testData, k=5) correct = np.count_nonzero(result == labels) accuracy = correct*100.0/10000 print accuracy

The 2nd code snippet (for the English alphabet) takes input from a .data file in the following format:

T,2,8,3,5,1,8,13,0,6,6,10,8,0,8,0,8 I,5,12,3,7,2,10,5,5,4,13,3,9,2,8,4,10 D,4,11,6,8,6,10,6,2,6,10,3,7,3,7,3,9 N,7,11,6,6,3,5,9,4,6,4,4,10,6,10,2,8 G,2,1,3,1,1,8,6,6,6,6,5,9,1,7,5,10 S,4,11,5,8,3,8,8,6,9,5,6,6,0,8,9,7 B,4,2,5,4,4,8,7,6,6,7,6,6,2,8,7,10

...there's about 20,000 lines of that. The data describes contours of characters.