Handwritten Equations Solver Using Convolution Neural Network

Mathematics is fundamental to everyone’s existence. We typically use calculators embedded into our current devices to solve mathematical equations. This project aims to develop a Handwritten Equation Solver that can solve mathematical equations written by a wide range of people. A neural network approach is used to recognize symbols and alphabets. Among other things, the system can recognize alphabets, all integers, mathematical symbols, and some special symbols. When one or more variables’ coefficients are multiplied by their sum of powers, the result is an algebraic expression known as a polynomial. If x is variable, ax+b=0 then it is linear polynomial equation. We show how computers can learn cognitive skills that allow them to understand an issue based on its visual context. The problem is simplified by using an image of handwritten polynomial equations as input and producing the answer as output. The proposed workflow method automatically simplifies the Handwritten polynomial equation. Horizontal compact projection analysis and combination linked component analysis approaches are used for segmentation. To classify individual characters, a Convolution Neural Network is utilized. For the solution of the problem, a character string operation is used for each proper detection. It’s also quite good at what it does. For the solution of the equation, each proper detection, character string operation is employed. Finally, the experimental findings reveal that our proposed approach is really effective.