A novel method for off-line handwriting-based writer identification

Handwriting-based writer identification is a hot research topic in the pattern recognition field. Nowadays, on-line handwriting-based writer identification is steadily growing toward its maturity. On the contrary, off-line handwriting-based writer identification still remains as a challenging problem because writing features only can be extracted from the handwriting image in this situation. As a result, plenty of dynamic writing information, which is very valuable for writer identification, is lost. At present, 2-D Gabor filter method is widely acknowledged as a good method for off-line handwriting identification, however it still suffers from some inherent disadvantages, such as the high computational cost. In this paper, we present a novel wavelet-based GGD method to replace the traditional 2-D Gabor filters. Shown in our experiments, this novel method not only achieves better experiment results but also greatly reduces the elapsed time on calculation.