Personal Handwriting Identification Based on PCA

In this paper, a novel algorithm is presented for writer identification from handwritings. Principal Component Analysis is applied to the gray-scale handwriting images to find a set of individual words which best characterize a person’s handwriting style and have maximal difference from other people style. During identification, we only need to utilize a set of individual characteristic words for comparison, instead of comparing the whole handwriting text to identify the writers. So not only is a very high average identification performance of 97.5% obtained, but also a very fast identification speed is achieved in our method. In the experiment, 400 pages of handwriting texts, containing almost 16000 Chinese words written by 40 different writers are used to validate the performance of the method.