HMM-Based Off-Line Uyghur Signature Recognition

Signature as a new biometric-based feature, due to its convenience, reliability, and non-invasion, signature recognition has been accepted by people. It is widely used in many fields such as commercial, financial, judicial, insurance and other aspects, so offline signature recognition has important theoretical significance and practical value. In this paper, an offline signature recognition system based on Hidden Markov Models is established to extract the DCT features of off-line signatures. This method takes all the fonts in the offline signature image as a whole, uses image processing techniques to segment the entire font area, and then calculates the number of pixels in each font part. The whole is modeled by a Hidden Markov Model, the best state chain is obtained using viterbi segmentation, and the EM algorithm is used to train the model. There are 2000 Uyghur signatures from 100 different people, 1000 English signatures from 50 different people, the highest recognition rates were 99.5% and 97.5%, respectively. The experimental results show that Hidden Markov Model can accurately describe the characteristics of Uygur signatures.