  Handwritten Mathematical Expression Recognition (HMER) is a challenging task
with many educational applications. Recent methods for HMER have been developed
for complex mathematical expressions in standard horizontal format. However,
solutions for elementary mathematical expression, such as vertical addition and
subtraction, have not been explored in the literature. This work proposes a new
handwritten elementary mathematical expression dataset composed of addition and
subtraction expressions in a vertical format. We also extended the MNIST
dataset to generate artificial images with this structure. Furthermore, we
proposed a solution for offline HMER, able to recognize vertical addition and
subtraction expressions. Our analysis evaluated the object detection algorithms
YOLO v7, YOLO v8, YOLO-NAS, NanoDet and FCOS for identifying the mathematical
symbols. We also proposed a transcription method to map the bounding boxes from
the object detection stage to a mathematical expression in the LATEX markup
sequence. Results show that our approach is efficient, achieving a high
expression recognition rate. The code and dataset are available at
https://github.com/Danielgol/HME-VAS
