TELKOMNIKA Telecommunication Computing Electronics and Control
One-shot learning Batak Toba character recognition using siamese neural network

TELKOMNIKA Telecommunication Computing Electronics and Control
One-shot learning Batak Toba character recognition using siamese neural network

Batak
Character recognition
Convolutional neural network
One-shot learning
Siamese neural network

Siamese neural network (SINN) is an image processing model that compares
the scores of two patterns. The SINN algorithm is a combination of the use
of the double convolutional neural network (CNN) algorithm. By combined
SINN with a one-shot learning algorithm, we can build an image model
without requiring thousands of images for training. The test results from the
SINN algorithm and one-shot learning show that this process was successful
in matching the two data but was unable to produce labels from the data
being tested. Because of this, the researcher decided to continue the
implementation process using the CNN algorithm combined with single shot
detection (SSD). By using a dataset of 5000, the recognition and translation
of the Toba Batak script was successful. The percentage of average accuracy
results from CNN and SSD in recognizing Toba Batak characters is 84.08%
for single characters and 74.13% for mixed characters. While the percentage
of average accuracy results for testing the breadth first search algorithm is
75.725%.

Yohanssen Pratama, Sarah Try Novelitha Nainggolan, Desy Isabel Nadya, Nova Yanti Naipospos

http://telkomnika.uad.ac.id

Feb 04, 2022

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