Chinese Handwritten Writer Identification based on Structure Features and Extreme Learning Machine

In this paper,we propose a new approach for writer identification of Chinese handwritten.In our method, we deal with writer identification of Chinese handwritten using Chinese character structure features(CSF) and extreme learning machine(ELM).To extract the features embedded in Chinese handwriting characters, special structures have been explored according to the trait of Chinese handwriting characters,where 20 features are extracted from the structures, these features constitute patterns of writer handwriting. We also combine structure features with extreme learning machine (ELM) as a new scheme for writer recognition, ELM is single hidden layer feed forward networks (SLFN), which randomly chooses the input weights and analytically determines the output weights of SLFN. This algorithm learns much faster than traditional popular learning algorithms. Experimental results demonstrate CSF/ELM method can achieve better performance than other traditional schemes for writer identification.