TELKOMNIKA Telecommunication, Computing, Electronics and Control
Handwriting identification using deep convolutional neural network method

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Handwriting identification using deep convolutional neural network method

Biometrics, Convolutional neural network, Transfer learning, Writer identification

Handwriting is a unique thing that produced differently for each person.
Handwriting has a characteristic that remain the same with single writer,
so a handwriting can be used as a variable in biometric systems. Each person have a different form of handwriting style but with a small possibility that same characters have something commons. We propose a handwriting identification method using sentence segmented handwriting forms. Sentence form is used to get more complete handwriting characteristics than using a single characters or words. Dataset used is divided into three categories of images, binary, grayscale, and inverted binary. All datasets have same image with different in color and consist of 100 class. Transfer learning used
in this paper are pre-trained model VGG19. Training was conducted in
100 epochs. Highest result is grayscale images with genuince acceptance rate of 92.3% and equal error rate of 7.7%.

Oka Sudana, I Wayan Gunaya, I Ketut Gede Darma Putra

DOI: 10.12928/TELKOMNIKA.v18i4.14864

Universitas Ahmad Dahlan

August 2020

Sri Wahyuni

ISSN: 1693-6930

http://journal.uad.ac.id/index.php/TELKOMNIKA

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TELKOMNIKA Telecommunication, Computing, Electronics and Control