  Handwriting recognition technology allows recognizing a written text from a
given data. The recognition task can target letters, symbols, or words, and the
input data can be a digital image or recorded by various sensors. A wide range
of applications from signature verification to electronic document processing
can be realized by implementing efficient and accurate handwriting recognition
algorithms. Over the years, there has been an increasing interest in
experimenting with different types of technology to collect handwriting data,
create datasets, and develop algorithms to recognize characters and symbols.
More recently, the OnHW-chars dataset has been published that contains
multivariate time series data of the English alphabet collected using a
ballpoint pen fitted with sensors. The authors of OnHW-chars also provided some
baseline results through their machine learning (ML) and deep learning (DL)
classifiers.
  In this paper, we develop handwriting recognition models on the OnHW-chars
dataset and improve the accuracy of previous models. More specifically, our ML
models provide $11.3\%$-$23.56\%$ improvements over the previous ML models, and
our optimized DL models with ensemble learning provide $3.08\%$-$7.01\%$
improvements over the previous DL models. In addition to our accuracy
improvements over the spectrum, we aim to provide some level of explainability
for our models to provide more logic behind chosen methods and why the models
make sense for the data type in the dataset. Our results are verifiable and
reproducible via the provided public repository.
