<p>This paper presents novel methods to improve feature extraction and recognition capabilities in handwritten mathematical expression recognition (HMER). By introducing a multi-scale residual (MSR) module within a DenseNet encoder, we effectively capture detailed and global features across different scales, thus overcoming feature loss problems commonly encountered in HMER tasks. In addition, we propose a data augmentation strategy based on spatial transformations to increase feature diversity without additional data. Our methodology is extensively evaluated on the CROHME 2014, 2016, and 2019 datasets, achieving recognition accuracies of 56.75%, 53.79%, and 56.13%, respectively, demonstrating consistent improvements in accuracy and robustness over traditional methods. This approach further optimizes the overall performance of the model, making it well-suited for real-world applications requiring high accuracy in the recognition of handwritten mathematical expressions. All source code and datasets are accessible at <a href="https://github.com/freedompuls/MsMER">https://github.com/freedompuls/MsMER</a>, facilitating reproducibility. This work advances the state of the art in HMER and provides valuable insights for researchers and practitioners in image processing and pattern recognition.</p>