  Recognition of Handwritten Mathematical Expressions (HMEs) is a challenging
problem because of the ambiguity and complexity of two-dimensional handwriting.
Moreover, the lack of large training data is a serious issue, especially for
academic recognition systems. In this paper, we propose pattern generation
strategies that generate shape and structural variations to improve the
performance of recognition systems based on a small training set. For data
generation, we employ the public databases: CROHME 2014 and 2016 of online
HMEs. The first strategy employs local and global distortions to generate shape
variations. The second strategy decomposes an online HME into sub-online HMEs
to get more structural variations. The hybrid strategy combines both these
strategies to maximize shape and structural variations. The generated online
HMEs are converted to images for offline HME recognition. We tested our
strategies in an end-to-end recognition system constructed from a recent deep
learning model: Convolutional Neural Network and attention-based
encoder-decoder. The results of experiments on the CROHME 2014 and 2016
databases demonstrate the superiority and effectiveness of our strategies: our
hybrid strategy achieved classification rates of 48.78% and 45.60%,
respectively, on these databases. These results are competitive compared to
others reported in recent literature. Our generated datasets are openly
available for research community and constitute a useful resource for the HME
recognition research in future.
