Research On Handwritten Signature Identification Based On LSTM Neural Network

Under the background of the mobile Internet era,with the upgrading of the office mode of all walks of life,nowadays,many office environments are equipped with computers,tablets,smart phones,and other devices,paperless office is realized through the Internet.In paperless office process,the document signature is no longer a paper signature but an electronic hand written signature.If there is no fast and reliable automatic signature identification technology,it will have a significant impact on office efficiency and personal information security.Handwriting identification is an important branch of biometric identification technology.Many scholars have made great achievements in this field using traditional pattern recognition technology,but there are still some problems,such as feature selection and imitation signature.In recent years,the popularity and rapid development of deep learning technology have solved many problems which have plagued the research of face recognition,speech recognition and Natural Language Processing.However,there is almost no research in this field of signature identification.In this paper,a time recurrent neural network(Recurrent Neural Network,RNN)model based on Long Short-Term Memory(LSTM)unit is applied to the field of online signature identification,and a set of signature identification system with practical application value is designed and developed.The main contents of this paper are as follows:(1)Referring to the related handwriting data set at home and abroad,a handwritten signature database suitable for this paper is set up.A signature data acquisition system based on Android platform is designed and developed.By using App,static signature data and dynamic signature data can be collected at the same time,which can be provided for future signature identification research.Rich sample material and basic platform.(2)A multi-layer recurrent neural network model based on LSTM unit is established to extract more advanced dynamic signature data features for signature identification.In view of the gradient longitudinal propagation resistance that the longitudinal stacked LSTM unit may encounter during the training process,a gradient smooth propagation between the hidden layers is added.New path.(3)When the dynamic signature data collected in the signature collection system is preprocessed,the algorithm is designed solves the problem that the number distribution on the time axis is inconsistent due to the difference of the complexity of the signed Chinese characters and the personal writing habit,and the initial special collection extraction algorithm is designed after the preprocessing to extracte original data for input to the experimental model(4)A handwritten signature verification experiment based on LSTM model is designed.Through a large number of experiments,the experimental model is improved to explore the influence of different network parameters on the experimental results,and the optimal network parameters suitable for the experimental model are selected.