Biometric personal identification based on handwriting

In this paper, we describe a new method to identify the writer of Chinese handwriting documents. There are many methods for signature verification or writer identification, but most of them require segmentation or connected component analysis. They are the kinds of content dependent identification methods as signature verification requires the writer to write the same text (e.g. his name). In our new method, we take the handwriting as an image containing some special texture, and writer identification is regarded as texture identification. This is a content independent method. We apply fthe well-established 2-D Gabor filtering technique to extract features of such textures and a Weighted Euclidean Distance classifier to fulfil the identification task. Experiments are made using Chinese handwritings from 17 different people and very promising results were achieved.