Off-Line Signature Recognition Based on Angle Features and Artificial Neural Network Algorithm  

作　　者：Laila Y.Fannas Ahmed Y.Ben Sasi 

机构地区：[1]the Faculty of Information Technology,the University of Misurata [2]the College of Industrial Technology

出　　处：《Journal of Electronic Science and Technology》2014年第1期85-89,共5页电子科技学刊（英文版）

基　　金：supported by the University of Misurata,Libya and the College of Industrial Technology,Libya

摘　　要：Handwritten signature recognition is presented based on an angle feature vector by using the artificial neural network （ANN） in this research. Each signature image will be represented by an angle vector. The feature vector will constitute the input to the ANN. The collection of signature images is divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested by recognizing the signatures. When a signature is classified correctly, it is considered correct recognition, otherwise it is a failure. The achieved recognition rate of this system is 94%.Handwritten signature recognition is presented based on an angle feature vector by using the artificial neural network （ANN） in this research. Each signature image will be represented by an angle vector. The feature vector will constitute the input to the ANN. The collection of signature images is divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested by recognizing the signatures. When a signature is classified correctly, it is considered correct recognition, otherwise it is a failure. The achieved recognition rate of this system is 94%.

关 键 词：Angle features artificial neuralnetwork signature recognition. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]