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...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%.展开更多
基金supported by the University of Misurata,Libya and the College of Industrial Technology,Libya
文摘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%.
