Manuscript received June 6, 2022; revised July 21, 2022; accepted August 11, 2022.
Abstract—In pattern recognition fields, it is worthwhile to
develop a pattern recognition system that hears one and
knows ten. Recently, classification of printed characters that
are the same fonts is almost possible, but classification of
handwritten characters is still difficult. On the other hand,
there are a large number of writing systems in the world,
and there is a need for efficient character classification even
with a small sample. Deep learning is one of the most
effective approaches for image recognition. Despite this,
deep learning causes overtrains easily, particularly when the
number of training samples is small. For this reason, deep
learning requires a large number of training samples.
However, in a practical pattern recognition problem, the
number of training samples is usually limited. One method
for overcoming this situation is the use of transfer learning,
which is pretrained by many samples. In this study, we
evaluate the generalization performance of transfer learning
for handwritten character classification using a small
training sample size. We explore transfer learning using a
fine-tuning to fit a small training sample. The experimental
results show that transfer learning was more effective for
handwritten character classification than convolution
neural networks. Transfer learning is expected to be one
method that can be used to design a pattern recognition
system that works effectively even with a small sample.
Keywords—small training sample sizes, handwritten
character classification, transfer learning, fine-tuning,
convolution neural networks
Cite: Yoshihiro Mitani, Naoki Yamaguchi, Yusuke Fujita, and Yoshihiko Hamamoto, "Evaluation of Transfer Learning for Handwritten Character Classification Using Small Training Samples," Journal of Image and Graphics, Vol. 11, No. 1, pp. 21-25, March 2023.
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