Performance Investigation of Handwritten Equation Solver using CNN for Betterment

Abstract

Identifying strong handwritten characters is a difficult task in the field of medical field and it is tedious process on decoding handwritten medicines. Recognition of handwritten mathematical expressions is a complex issue. The distribution and classification of specific characters makes the task more difficult. In our project, handwritten numbers and symbols are read and further addition, subtraction and multiplication operations are performed. The project includes a study about model deployment using convoluted neural networks and flasks. We use the CNN to classify specific characters. Tracking of character string operations are used to solve equations. The maximum accuracy of the proposed model is 99.12% recall is 95%, sensitivity is 89% and specificity is 68%. Effectiveness of our proposed system is helpful for students who want to get handwritten answers. The equation can be extended to more complex equations and more user data can be trained to improve correction and accuracy.