Multilingual Offline Handwritten Signature Recognition Based on Statistical Features
Signature recognition is an identity authentication method widely used in various fields such as finance, judiciary, banking, insurance so on, and it plays an important role in society as a behavioral trait. In order to improve the accuracy of multilingual off-line handwritten signature recognition, this paper was proposed the high-dimensional statistical feature extraction methods to multi signature samples. The signature image is preprocessed firstly. Then, 128 dimensional local center point features and 112 dimensional ETDT features were extracted from the mixture (English, Chinese and Uyghur) signatures, and a high dimensional feature vector is formed after combining this two features. At last, the different distance based metric learning methods were used to train and recognize the multilingual signature. It was obtained 91.50%, 95.75% and 97.50% of recognition rates respectively using mixed Chinese-English signature dataset, Chinese-Uyghur mixed signature dataset and the English-Uyghur mixed signature dataset separately. The experimental results indicated that the algorithm proposed in this paper can identify mixed signature effectively, and it is suitable for identifying multilingual handwritten signature.
KeywordsHandwritten signature Multilingual signature Distance metric learning Statistical features
This work was supported by the National Natural Science Foundation of China (No. 61563052, 61163028, 61363064), the Funds for Creative Research Groups of Higher Education of Xinjiang Uyghur Autonomous Region (XJEDU2017T002).
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