Multi-Feature Learning by Joint Training for Handwritten Formula Symbol Recognition

Multi-Feature Learning by Joint Training for Handwritten Formula Symbol Recognition

Given the similarity of handwritten formula symbols and various handwriting styles, this paper proposes a squeeze-extracted multi-feature convolution neural network (SE-MCNN) to improve the recognition rate of handwritten formula symbols. The system proposed in this paper integrates the eight-direct...

Full description

Bibliographic Details
Main Authors: Dingbang Fang, Chenhao Zhang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Artificial neural network
multi-feature
joint training
discriminative feature
Online Access:https://ieeexplore.ieee.org/document/9027943/

Internet

https://ieeexplore.ieee.org/document/9027943/

Similar Items