A novel feature extraction method and hybrid tree classification for handwritten numeral recognition
Pattern Recognition Letters
Quantifying the impact of learning algorithm parameter tuning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
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A comparison is made of two techniques for recognizing numeric handprint characters using a variety of features including 2D fast Fourier transform coefficients, geometrical moments, and topological features. A backpropagation network and a nearest neighbor classifier are evaluated in terms of recognition performance and computational requirements. The results indicate that for complex problems, the neural network performs comparably to the nearest-neighbor classifier while being significantly more cost effective