# Recognizing Handwritten Mathematical Expressions of Vertical Addition and Subtraction

arXiv: https://arxiv.org/abs/2308.05820 · HTML: https://mlsanity.com/paper/2308.05820/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Syntax-Aware Network for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2203.01601/) (2022)
- [Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/1801.03530/) (2018)
- [When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2207.11463/) (2022)
- [CoMER: Modeling Coverage for Transformer-based Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2207.04410/) (2022)
- [Handwritten Mathematical Expression Recognition via Attention Aggregation based Bi-directional Mutual Learning](https://mlsanity.com/paper/2112.03603/) (2021)
- [A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey](https://mlsanity.com/paper/2208.10895/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Mixed User-Centered Approach to Enable Augmented Intelligence in Intelligent Tutoring Systems: The Case of MathAIde app](https://mlsanity.com/paper/2508.00103/) (2025)

## Abstract (arXiv)

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