Recognizing Handwritten Mathematical Expressions via Paired Dual Loss Attention Network and Printed Mathematical Expressions
Abstract
Recognition of Handwritten Mathematical Expressions (HMEs) is a challenging problem because of the complicated structure and uncommon math symbols contained in HMEs. Moreover, the lack of training data is a serious issue, especially for deep learning-based systems. In this paper, we proposed a dual loss attention model that utilizes the existing latex corpus to improve accuracy. The proposed dual loss attention has two losses, including decoder loss and context matching loss to learn semantic invariant features for the encoder and latex grammar for the decoder from handwritten and printed MEs. The results of experiments on the CROHME 2014 and 2016 databases demonstrate the superiority and effectiveness of our proposed model. These results are competitive compared to others reported in recent literature.
Cite
Text
Le. "Recognizing Handwritten Mathematical Expressions via Paired Dual Loss Attention Network and Printed Mathematical Expressions." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00291Markdown
[Le. "Recognizing Handwritten Mathematical Expressions via Paired Dual Loss Attention Network and Printed Mathematical Expressions." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/le2020cvprw-recognizing/) doi:10.1109/CVPRW50498.2020.00291BibTeX
@inproceedings{le2020cvprw-recognizing,
title = {{Recognizing Handwritten Mathematical Expressions via Paired Dual Loss Attention Network and Printed Mathematical Expressions}},
author = {Le, Anh Duc},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2020},
pages = {2413-2418},
doi = {10.1109/CVPRW50498.2020.00291},
url = {https://mlanthology.org/cvprw/2020/le2020cvprw-recognizing/}
}