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 algorithm can accurately read handwritten digits.
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Hi,
When I execute the code I get the following cv2.error:
error: (-215:Assertion failed) test_samples.type() == CV_32F && test_samples.cols == samples.cols in function ‘cv::ml::BruteForceImpl::findNearest’
What can be the reason?
How can we add another set like test_digits.png if we want to and how we can
Hi,
When I execute the code I get the following cv2.error:
error: (-215:Assertion failed) test_samples.type() == CV_32F && test_samples.cols == samples.cols in function ‘cv::ml::BruteForceImpl::findNearest’
What can be the reason?
This is because the location of 1 or both of your png files are not correct… Please enter the location carefully…
thanks for the example. Please could you please help me to add real time recognition?
Good work! Run like a magic.
how can I make a custome dataset for trainging?
hello
pls if you found out how please share it with me and thank you in advance