# Improving Handwritten Mathematical Expression Recognition with CNN by Exploring Augmentation, Regularization, and Training Strategies
> Sunardi

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_105025411979
Jurnal / Konferensi: 2025 IEEE International Conference on Artificial Intelligence and Mechatronics Systems Aims 2025
Tahun terbit: 2025
DOI: https://doi.org/10.1109/AIMS66189.2025.11229463
Citations: 0

## Authors
- Sunardi

## Abstract
This scholarly investigation constructs a framework predicated on Convolutional Neural Networks (CNN) for the recognition of handwriting in the context of mathematical problem-solving at the elementary educational tier, encompassing numerals 0-9 and fundamental mathematical symbols including addition, subtraction, multiplication, and division. Employing a CNN-based Sequencing Architecture, the developed system demonstrates the capability to identify, organize, and structure handwritten mathematical expressions authored by students with an impressive accuracy rate of 99.07%. The implementation of this system accelerates the evaluation process, mitigates human errors associated with grading, and furnishes immediate feedback. Furthermore, this research delves into the formulation and examination of diverse data augmentation methodologies, regularization techniques, and training strategies aimed at enhancing the model’s performance. The application of augmentations such as rotation, flipping, and image displacement, along with regularization measures incorporating dropout and batch normalization, significantly bolsters the model’s capacity for generalization. The overarching objective of this research is to augment the efficiency of mathematics learning assessments and to advocate for the integration of artificial intelligence technologies within primary educational settings.

## Keywords
- Computer science
- Convolutional neural network
- Artificial intelligence
- Regularization (linguistics)
- Handwriting recognition
- Numeral system
- Artificial neural network
- Dropout (neural networks)
- Context (archaeology)
- Handwriting
- Machine learning
- Pattern recognition (psychology)
- Expression (computer science)
- Facial expression recognition
- Convolution (computer science)
- Training set
- Image (mathematics)
- Activity recognition
- Deep learning

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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