The use of convolutional neural networks (CNNs) has accelerated the progress
of handwritten character classification/recognition. Handwritten character
recognition (HCR) has found applications in various domains, such as traffic
signal detection, language translation, and document information extraction.
However, the widespread use of existing HCR technology is yet to be seen as it
does not provide reliable character recognition with outstanding accuracy. One
of the reasons for unreliable HCR is that existing HCR methods do not take the
handwriting styles of non-native writers into account. Hence, further
improvement is needed to ensure the reliability and extensive deployment of
character recognition technologies for critical tasks. In this work, the
classification of English characters written by non-native users is performed
by proposing a custom-tailored CNN model. We train this CNN with a new dataset
called the handwritten isolated English character (HIEC) dataset. This dataset
consists of 16,496 images collected from 260 persons. This paper also includes
an ablation study of our CNN by adjusting hyperparameters to identify the best
model for the HIEC dataset. The proposed model with five convolutional layers
and one hidden layer outperforms state-of-the-art models in terms of character
recognition accuracy and achieves an accuracy of $\mathbf{97.04}$%. Compared
with the second-best model, the relative improvement of our model in terms of
classification accuracy is $\mathbf{4.38}$%.

利用卷积神经网络（CNNs）对非母语使用者书写的英文字母进行分类识别，借助 HIEC 数据集训练了一个定制的 CNN 模型，并通过调整超参数进行了消融研究，结果显示该模型在字符识别准确性方面优于其他模型，达到了 97.04% 的准确度，相对于次优模型提升了 4.38%。