  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.
