Base and Exponent Prediction in Mathematical Expressions using Multi-Output CNN
This work addresses the challenge of accurate mathematical expression recognition in noisy real-world conditions, but it is incremental as it simplifies existing CNN approaches for a specific task.
The research tackled the problem of predicting base and exponent values from images of mathematical expressions by developing a multi-output CNN model trained on 10,900 synthetically generated images with noise and variations, achieving high accuracy and efficient training time.
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.