基于多重几何特征和CNN的脱机手写算式识别

针对中小学数学课堂中具有复杂二维空间结构的手写算式, 提出了一种基于多重几何特征和卷积神经网络(CNN)的脱机手写算式识别的解决方案. 首先, 基于CNN分类算法, 对图像预处理后的单个手写字符进行识别; 然后, 利用几何特征, 如宽高比、质心坐标、质心偏移角度、中心偏移量、水平重叠区间比等, 识别具有复杂空间结构的小数、分数、指数、根式等常见手写算式, 并采用分治算法完成由以上算式组合嵌套的复合算式识别; 最后, 设计并实现脱机手写算式识别系统. 实验结果表明: 在满足一定光照条件下, 该方案对不同分辨率、含噪声图像的手写算式识别率可达90.43%, 具有一定的应用价值.

Off-Line Handwritten Equation Recognition Based on Multiple Geometric Features and CNN

In view of the handwritten equation with complex two-dimensional spatial structure in the mathematics class of primary and secondary schools, this study proposes a solution of off-line handwritten equation recognition based on multiple geometric features and Convolutional Neural Network (CNN). First, based on CNN classification algorithm, the single handwritten character is recognized after image preprocessing. Then, using geometric features, such as aspect ratio, center of mass coordinate, center of mass offset angle, center offset, horizontal overlap interval ratio, etc., to recognize common handwritten formulas such as decimal, fraction, index, and root formula with complex spatial structure, and using the divide-and-conquer algorithm to complete the recognition of composite formulas nested by the above formula combination. Finally, the off-line handwritten arithmetic recognition system is designed and implemented. The experimental results show that under certain illumination conditions, the recognition rate of handwritten equation of different resolutions and noisy images can reach 90.43%, which has certain application value.