Handwritten formula data enhancement based on Edmonds' algorithm

Zhongjian Hong; Junzhao Zhang; Shuai Wang; Haodi Zhao; Chen Wang; Fan Li; Xiaofeng Lu

doi:10.1117/12.3011499

7 December 2023 Handwritten formula data enhancement based on Edmonds' algorithm

Zhongjian Hong, Junzhao Zhang, Shuai Wang, Haodi Zhao, Chen Wang, Fan Li, Xiaofeng Lu

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129410Q (2023) https://doi.org/10.1117/12.3011499
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

This paper describes a tree-based decomposition and subexpression exchange method for generating new strategically valid handwritten mathematical expressions (HMEs). This approach uses the Edmonds algorithm to extract a Minimum Spanning Tree (MST) from a directed view and groups input strokes to segment symbols and label spatial relationships between symbols and symbol pairs. By training the HME recognition model and the mathematical Language Model (LM), we aim to improve the recognition performance and generalization ability of existing state-of-the-art methods from both data enhancement and model design perspectives. By adding HMEs of different styles, complexity and difficulty levels in the training dataset in order to increase the adaptability of the system to various forms of expressions.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhongjian Hong, Junzhao Zhang, Shuai Wang, Haodi Zhao, Chen Wang, Fan Li, and Xiaofeng Lu "Handwritten formula data enhancement based on Edmonds' algorithm", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129410Q (7 December 2023); https://doi.org/10.1117/12.3011499

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available