  Handwritten character recognition (HCR) is a challenging problem for machine
learning researchers. Unlike printed text data, handwritten character datasets
have more variation due to human-introduced bias. With numerous unique
character classes present, some data, such as Logographic Scripts or
Sino-Korean character sequences, bring new complications to the HCR problem.
The classification task on such datasets requires the model to learn
high-complexity details of the images that share similar features. With recent
advances in computational resource availability and further computer vision
theory development, some research teams have effectively addressed the arising
challenges. Although known for achieving high accuracy while keeping the number
of parameters small, many common approaches are still not generalizable and use
dataset-specific solutions to achieve better results. Due to complex structure,
existing methods frequently prevent the solutions from gaining popularity. This
paper proposes a highly scalable approach for detailed character image
classification by introducing the model architecture, data preprocessing steps,
and testing design instructions. We also perform experiments to compare the
performance of our method with that of existing ones to show the improvements
achieved.
