Recognizing Handwritten Mathematical Expressions of Vertical Addition and Subtraction
Daniel Rosa, Filipe R. Cordeiro, Ruan Carvalho, Everton Souza, Sergio, Chevtchenko, Luiz Rodrigues, Marcelo Marinho, Thales Vieira, Valmir, Macario

TL;DR
This paper introduces a new dataset and method for recognizing handwritten vertical addition and subtraction expressions, demonstrating high accuracy with object detection algorithms and a novel transcription approach.
Contribution
The work presents the first dataset and recognition solution specifically for handwritten vertical elementary mathematical expressions.
Findings
High recognition accuracy achieved with object detection algorithms.
Effective mapping from detected symbols to LATEX expressions.
Dataset and code publicly available for further research.
Abstract
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…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Natural Language Processing Techniques · Teaching and Learning Programming
MethodsConvolution · 1x1 Convolution · Feature Pyramid Network · Non Maximum Suppression · FCOS
