Question

Using the Nearest Neighbor Rule and mesh feature, perform character
recognition in opencv. Characters to be recognized include alphabetical letters, numerics, Hiragana, or Kanji
(Chinese characters) etc. from input image jpeg into traning data set using simple AI protocol

Answer 1

Digit Recognition -

import numpy as np

import cv2 as cv

img = cv.imread('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 )

English alphabets recognition -

import cv2 as cv

import numpy as np

# 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 = 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 )