CNNCalc – An Implementation of a Handwritten Mathematical Equation Solver

Creating a reliable handwritten equation solver using Convolutional Neural Networks (CNN) is a challenging task in image processing and classification. The recognition of handwritten mathematical expressions is one of the most complex challenges in computer vision research because certain characters are segmented and classified, such as polynomial, quadratic, and other mathematical equations. The aim of this study is to develop an application that can solve handwritten equations (arithmetic, quadratic, and trigonometric) and logical operations (logical AND, OR, NOT, NAND, XOR, NOR) using CNN with image processing techniques to achieve high accuracy. Additionally, the application can extract text from images, and users can learn and share educational content on the platform to enhance their knowledge. One of the main advantages of online learning is that it allows users to access updated content and media at any time. The platform also enables users to take tests on various topics and receive instant feedback Users can code in various languages, including C, Python, and Java. Furthermore, the application provides a way for users to monitor their performance and improve their skills continually. The approach used in this project involves horizontal compact projection analysis and a survey for segmentation and binarization. Connected component analysis and integrated connected component analysis methodologies are utilized for character classification using CNN. Character string operation is used for detecting characters in the polynomial. The experimental results demonstrate the effectiveness of this approach in solving a wide range of mathematical equations. This application aims to make learning an enjoyable experience for students, especially in today’s digital age.