Knn handwritten digits recognition – OpenCV 3.4 with python 3 Tutorial 36

In this video you will find an easy explanation of how the KNN algorythm works for handwritten digits recognition.
We use a sample of 2500 digits (250 of each type 0 to 9) to train the algorythm and we have another small sample to test it and see if the Knn algorythm can accurately read handwritten digits.

Source code:

import cv2
import numpy as np

digits = cv2.imread("digits.png", cv2.IMREAD_GRAYSCALE)
test_digits = cv2.imread("test_digits.png", cv2.IMREAD_GRAYSCALE)

rows = np.vsplit(digits, 50)
cells = []
for row in rows:
    row_cells = np.hsplit(row, 50)
    for cell in row_cells:
        cell = cell.flatten()
        cells.append(cell)
cells = np.array(cells, dtype=np.float32)

k = np.arange(10)
cells_labels = np.repeat(k, 250)


test_digits = np.vsplit(test_digits, 50)
test_cells = []
for d in test_digits:
    d = d.flatten()
    test_cells.append(d)
test_cells = np.array(test_cells, dtype=np.float32)


# KNN
knn = cv2.ml.KNearest_create()
knn.train(cells, cv2.ml.ROW_SAMPLE, cells_labels)
ret, result, neighbours, dist = knn.findNearest(test_cells, k=3)


print(result)

 

Files:

1. digits.png
2. test_digits.png