Automated Handwritten Equation Evaluation

Automated Handwritten Equation Evaluation

Author Name : Hemlata Kosare , Roshan Rajurkar , Karan Mandhare , Priyash Manwatkar , Rasik Moon , Resham Gaidhane , Vaibhav Ladhi

DOI: https://doi.org/10.56025/IJARESM.2023.11112365

ABSTRACT In the digital age, the use of neural networks (CNN) for automatic classification is revolutionary. This research introduces a new method that uses the power of deep learning to recognize and understand mathematical text. By segmenting and analyzing characters and operators, our system bridges the gap between textual equations and semantic analysis, ultimately allowing the evaluation of complex equations. The use of CNN makes it possible to adapt to different types of writing, and the integration of mathematics teaching with analytical strategies makes it easier to evaluate the accuracy of mathematical ideas. This research has the potential to revolutionize all fields such as educational technology, digital signage and smart teaching. In addition to the ability to identify and assess skills, the system must make mathematical concepts easy to understand and learn. Additionally, his ability to adapt to complex concepts and his good understanding of character development pave the way for a new era in computer vision. As we continue to develop and expand the capabilities of this model, it has the potential to impact many industries and improve the way we interact with and understand mathematics