TY - GEN
T1 - Refined and Locality-Enhanced Feature for Handwritten Mathematical Expression Recognition
AU - Yu, Liu
AU - Du, Xiangcheng
AU - Liu, Ziang
AU - Dong, Daoguo
AU - He, Liang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Many studies have been conducted on handwritten mathematical expression recognition (HMER) based on encoder-decoder architecture. However, the previous methods fail to predict accurate results due to low-quality images such as blur, complex background and distortion. In addition, ambiguous or subtle symbols caused by different handwriting styles are often recognized incorrectly. In this paper, we propose an efficient method for HMER to deal with the above issues. Specifically, we propose a Dual-branch Refinement Module (DRM) to deal with the challenging disturbances. In terms of ambiguous or subtle symbols, we believe that the combination of local and global information is beneficial to recognizing these symbols. Therefore, we design a Local Feature Enhancement Module (LFEM) to enhance local features, which can cooperate with global information extracted by the following transformer decoder. Extensive experimental results on CROHME and HME100K datasets verify the effectiveness of our method.
AB - Many studies have been conducted on handwritten mathematical expression recognition (HMER) based on encoder-decoder architecture. However, the previous methods fail to predict accurate results due to low-quality images such as blur, complex background and distortion. In addition, ambiguous or subtle symbols caused by different handwriting styles are often recognized incorrectly. In this paper, we propose an efficient method for HMER to deal with the above issues. Specifically, we propose a Dual-branch Refinement Module (DRM) to deal with the challenging disturbances. In terms of ambiguous or subtle symbols, we believe that the combination of local and global information is beneficial to recognizing these symbols. Therefore, we design a Local Feature Enhancement Module (LFEM) to enhance local features, which can cooperate with global information extracted by the following transformer decoder. Extensive experimental results on CROHME and HME100K datasets verify the effectiveness of our method.
KW - Handwritten mathematical expression recognition
KW - feature refinement
KW - local feature enhancement
KW - transformer
UR - https://www.scopus.com/pages/publications/85209191055
U2 - 10.1007/978-981-97-8511-7_3
DO - 10.1007/978-981-97-8511-7_3
M3 - 会议稿件
AN - SCOPUS:85209191055
SN - 9789819785100
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 30
EP - 43
BT - Pattern Recognition and Computer Vision - 7th Chinese Conference, PRCV 2024, Proceedings
A2 - Lin, Zhouchen
A2 - Zha, Hongbin
A2 - Cheng, Ming-Ming
A2 - He, Ran
A2 - Liu, Cheng-Lin
A2 - Ubul, Kurban
A2 - Silamu, Wushouer
A2 - Zhou, Jie
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024
Y2 - 18 October 2024 through 20 October 2024
ER -