EDSL: An Encoder-Decoder Architecture with Symbol-Level Features for Printed Mathematical Expression Recognition

• URL: http://arxiv.org/abs/2007.02517v1
• Date: Mon, 6 Jul 2020 03:53:52 GMT
• Title: EDSL: An Encoder-Decoder Architecture with Symbol-Level Features for Printed Mathematical Expression Recognition
• Authors: Yingnan Fu, Tingting Liu, Ming Gao, Aoying Zhou
• Abstract summary: We propose a new method named E, shorted for encoder-decoder with symbol-level features, to identify the printed mathematical expressions from images. E has achieved 92.7% and 89.0% in evaluation, which are 3.47% and 4.04% higher than the state-of-the-art method.
• Score: 23.658113675853546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Printed Mathematical expression recognition (PMER) aims to transcribe a printed mathematical expression image into a structural expression, such as LaTeX expression. It is a crucial task for many applications, including automatic question recommendation, automatic problem solving and analysis of the students, etc. Currently, the mainstream solutions rely on solving image captioning tasks, all addressing image summarization. As such, these methods can be suboptimal for solving MER problem. In this paper, we propose a new method named EDSL, shorted for encoder-decoder with symbol-level features, to identify the printed mathematical expressions from images. The symbol-level image encoder of EDSL consists of segmentation module and reconstruction module. By performing segmentation module, we identify all the symbols and their spatial information from images in an unsupervised manner. We then design a novel reconstruction module to recover the symbol dependencies after symbol segmentation. Especially, we employ a position correction attention mechanism to capture the spatial relationships between symbols. To alleviate the negative impact from long output, we apply the transformer model for transcribing the encoded image into the sequential and structural output. We conduct extensive experiments on two real datasets to verify the effectiveness and rationality of our proposed EDSL method. The experimental results have illustrated that EDSL has achieved 92.7\% and 89.0\% in evaluation metric Match, which are 3.47\% and 4.04\% higher than the state-of-the-art method. Our code and datasets are available at https://github.com/abcAnonymous/EDSL .

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