The Writer Identification Algorithm Based on Subspace

A new method using feature subspace for writer identification is proposed in this paper. The current writer identification algorithms are always that the more the extraction features the better the classifier result. However it will result in excessive calculational cost on classifier identification process. On the basis of these issues, after we obtain the high-dimensional features, first we extract the more useful features to compose of the subspace, and then carry on the identification process. It shows that this new method, compared with the classical method, not only achieves better identification results but also greatly reduces the elapsed time on computation of the identification process.