A practical pattern recognition system for translation, scale and rotation invariance

We present a practical pattern recognition system that is invariant with respect to translation, scale and rotation of objects. The system is also insensitive to large variations of the threshold used. As feature vectors, Zernike moments are used and we compare them with Hu's seven moment invariants. For a practical machine vision system, three key issues are discussed: pattern normalization, fast computation of Zernike moments, and classification using k-NN rule. As testing results, the system recognizes a set of 62 alphanumeric machine-printed characters with different sizes, at arbitrary orientations, and with different thresholds where the size of the characters varies from 10/spl times/10 to 512/spl times/512 pixels.<
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