Relation-Based Representation for Handwritten Mathematical Expression Recognition

Thanh-Nghia Truong, Quang Huy Ung, Hung Tuan Nguyen, Cuong Tuan Nguyen, Masaki Nakagawa

Abstract: This paper proposes relation-based sequence representation that enhances offline handwritten mathematical expressions (HMEs) recognition. Commonly, a LaTeX-based sequence represents the 2D structure of an HME as a 1D sequence. Consequently, the LaTeX-based sequence becomes longer, and HME recognition systems have difficulty in extracting its 2D structure. We propose a new representation for HMEs according to the relations of symbols, which shortens the LaTeX-based representation. We use an offline end-to-end HME recognition system that adopts weakly supervised learning to evaluate the proposed representation. Recognition experiments indicate that the proposed relation-based representation helps the HME recognition system achieve higher performance than the LaTeX-based representation. In fact, the HME recognition system achieves recognition rates of 53.35%, 52.14%, and 53.13% on the dataset of the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2014, 2016, and 2019, respectively. These results are more than 2 percentage points higher than the LaTeX-based system.