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Signature Recognition Using Backpropagation Artificial Neural Network Method
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A signature is a sign or symbol that is a miniature version of its owner. A signature is also a biometric feature that can be used to verify a person's identity. The signature used as a personal identification as well as the presence of a signature in a document states that the party who signed, knows and approves or as ratification of the entire contents of the document and becomes legal evidence. Signature recognition is done using an artificial neural network with backpropagation algorithm. In the backpropagation algorithm, signatures are trained to recognize a person's signature with some data such as target data, training data and test data. Then the network is tested for networking. The results of the application are used to recognize signature recognition using the backpropagation method obtained with different accuracy according to the original data obtained from feature extraction. Where the lowest accuracy is 30% and the highest accuracy is 100%
Title: Signature Recognition Using Backpropagation Artificial Neural Network Method
Description:
A signature is a sign or symbol that is a miniature version of its owner.
A signature is also a biometric feature that can be used to verify a person's identity.
The signature used as a personal identification as well as the presence of a signature in a document states that the party who signed, knows and approves or as ratification of the entire contents of the document and becomes legal evidence.
Signature recognition is done using an artificial neural network with backpropagation algorithm.
In the backpropagation algorithm, signatures are trained to recognize a person's signature with some data such as target data, training data and test data.
Then the network is tested for networking.
The results of the application are used to recognize signature recognition using the backpropagation method obtained with different accuracy according to the original data obtained from feature extraction.
Where the lowest accuracy is 30% and the highest accuracy is 100%.
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