Off-Line Handwritten Equation Recognition Based on Multiple Geometric Features and CNN
CSTR:
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    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.

    Reference
    Related
    Cited by
Get Citation

付鹏斌,彭荆旋,杨惠荣,李建君.基于多重几何特征和CNN的脱机手写算式识别.计算机系统应用,2020,29(8):271-279

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:February 14,2020
  • Revised:March 13,2020
  • Adopted:
  • Online: July 31,2020
  • Published: August 15,2020
Article QR Code
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063