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:
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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) |
How can we add another set like test_digits.png if we want to and how we can