Segmentation Approach: An Improved Solution for Solving Complex Mathematical Equations

Handwritten mathematical equation decoding and solving present major issues with far-reaching ramifications in the age of digital transformation and artificial intelligence. This research provides a novel method for the accurate recognition and effective solution of handwritten equations using Convolutional Neural Networks (CNN) coupled with Optical Character Recognition (OCR). The proposed system solves equations of different complexity with accuracy by using CNN -based classification, character segmentation, and picture pre-processing. When compared to traditional OCR models, experimental findings reveal gains in accuracy and efficiency, demonstrating the usefulness of the suggested technology. This work adds to the field of Artificial Intelligence (AI) applications in addition to improving the solution of handwritten equations. Through the use of character segmentation, CNN -based classification, and picture pre-processing, the system is able to solve equations with a wide range of complexity with impressive accuracy and speed. Experimental validation shows better performance than traditional OCR models, suggesting the method's efficacy in tackling the problems associated with solving handwritten equations. The results of this study offer promise for a number of fields needing precise and effective handwritten text processing, and they also advance artificial intelligence applications