The Design and Implementation of the Natural Handwriting Mathematical Formula Recognition System

Abstract

The traditional handwritten mathematical formula recognition mode has many shortcomings in recognition. For example, the recognition rate is low, the operation is complicated and other pain points. In order to make handwritten mathematical formula recognition more accurate and easy to use, and to solve the problem of low efficiency of editing data formulas for researchers engaged in mathematics-related professions. In this paper, a natural handwritten mathematical formula recognition system with one-click operation and higher recognition is implemented. The system uses a core algorithm to separate the target formulas based on histogram projection and dynamic comparison word method, and a three-layer convolutional neural network model based on CNN to recognize the segmented strings. Experiments show that the improved algorithm based on the algorithm has strong learning ability and robustness.