Conditional generative adversarial network-based online handwriting identification method

Abstract

The present invention requires to protect a conditional generative adversarial network-based online handwriting identification method. The method comprises the steps of 101 using a user registration module to register the basic information of a user; 102 using a reception module to receive a section of character information inputted by the user, wherein the information comprises the character writing style, the character writing strength and the character writing spacing; 103 training a conditional generative adversarial network on a handwriting signature data set by taking the category labels as the conditions, and being able to generate the corresponding directional digital features according to the information of the category labels; 104 using a handwriting identification module, using the conditional generative adversarial network to mine the personalized handwriting of the user and using an adversarial network signature discrimination model D which is a dichotomy device to discriminate whether the inputted data is the real handwriting data or a generated sample; 105 using an application module to apply the handwriting identification to an access control system and a plurality of user document signing scenes. The conditional generative adversarial network-based online handwriting identification method of the present invention has higher stability, safety and convenience, at the same time, can identify the handwriting style, strength and spacing information of the users by combining a conditional generative adversarial network method, and avoids the problem that the character features are not extracted completely.

Description

technical field [0001] The invention relates to the fields of image processing, pattern recognition and the like, and relates to an online handwriting recognition method based on a conditional generative confrontation network. Background technique [0002] At present, with the rapid development of the Internet of Things in China, there are threats of insecurity in the access control system, document signature, payment, and credit card payment. Aiming at the problems of poor recognition accuracy, easy imitation, and easy forgery in existing biometric authentication technologies, a method based on conditional generation is proposed. An Online Handwriting Recognition Approach to Adversarial Networks. This method aims to research and develop the security issues involved in access control systems and document signatures, and aims to design a security authentication method that facilitates the identification of personal identity information through handwriting features on terminal...

AI Technical Summary

Problems solved by technology

[0008] 1) Convergence problem;

[0009] 2) Difficult to train;

[0010] 3) The model is too free and uncontrollable;

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