Base and Exponent Prediction in Mathematical Expressions using Multi-Output CNN

TL;DR

This paper introduces a multi-output CNN model that accurately predicts bases and exponents in mathematical images, even under noisy and varied conditions, with efficient training and high robustness.

Contribution

It presents a simplified CNN approach for base and exponent prediction in mathematical images, trained on synthetic data with noise and variations, demonstrating high accuracy and efficiency.

Findings

High accuracy in predicting base and exponent values.

Robust performance on noisy and varied images.

Efficient training process.

Abstract

The use of neural networks and deep learning techniques in image processing has significantly advanced the field, enabling highly accurate recognition results. However, achieving high recognition rates often necessitates complex network models, which can be challenging to train and require substantial computational resources. This research presents a simplified yet effective approach to predicting both the base and exponent from images of mathematical expressions using a multi-output Convolutional Neural Network (CNN). The model is trained on 10,900 synthetically generated images containing exponent expressions, incorporating random noise, font size variations, and blur intensity to simulate real-world conditions. The proposed CNN model demonstrates robust performance with efficient training time. The experimental results indicate that the model achieves high accuracy in predicting the base and exponent values, proving the efficacy of this approach in handling noisy and varied input images.