The author conducted an experiment to compare the performance of a neural network against algorithmic approaches for recognizing handwritten digits from the MNIST dataset. The study evaluated a 'common sense' algorithm based on calculating Euclidean distance from average class images and a method utilizing Hu moments.
The 'common sense' algorithm, which compares test samples to an average image of each digit class, achieved 77% accuracy on 10,000 test images. When tested on digits drawn by the author, accuracy dropped to 20-30%. In comparison, the neural network achieved 98% accuracy on the test set.
The author also tested Hu moments, which are invariant to scale, rotation, and reflection. However, this approach yielded only 10% accuracy, effectively performing at the level of random guessing. The author concluded that Hu moments are unsuitable for digit recognition tasks.
The experiment highlighted the limitations of simple algorithmic models compared to neural networks for image recognition. The author noted that while neural networks require training, they consistently outperform manual algorithmic implementations in accuracy for this specific task.
