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Nonlinear normalization (NLN) by line density equalization has been popularly used in handwritten Chinese character recognition (HCCR). To overcome the intensive computation of local line density and the excessive shape distortion of NLN, we tested some alternative methods based on global transformation, including a moment-based linear transformation and two nonlinear methods based on quadratic curve fitting. The alternative methods are simpler in computation and the transformed images have more natural shapes. In experiments of HCCR on large databases, the alternative methods have yielded comparable or higher accuracies to the traditional NLN.