EDSL：一种用于印刷体数学表达式识别的符号级编码器-解码器结构
EDSL: An Encoder-Decoder Architecture with Symbol-Level Features for Printed Mathematical Expression Recognition
摘要：
印刷体数学表达式识别（PMER）旨在将印刷体数学表达式图像转录为结构化表达式，例如LaTeX表达式。它是一个至关重要的任务，许多应用，包括自动推荐问题，自动解决问题和分析的学生等。目前，主流的解决方案依赖于解决图像描述任务，所有解决图像摘要。因此，这些方法对于解决MER问题可能是次优的。本文提出了一种新的识别印刷体数学表达式的方法EDSL（encoder-decoder with symbol-level features）。EDSL的符号级图像编码器由分割模块和重建模块组成。通过执行分割模块，我们以无监督的方式从图像中识别所有符号及其空间信息。然后，我们设计了一个新的重建模块，以恢复符号分割后的符号依赖关系。特别地，我们采用位置校正注意力机制来捕捉符号之间的空间关系。为了减轻长输出带来的负面影响，我们采用Transformer模型将编码后的图像转换为序列化和结构化的输出。我们在两个真实数据集上进行了广泛的实验，以验证我们提出的EDSL方法的有效性和合理性。实验结果表明，EDSL在评价指标Match上分别达到了92.7%和89.0%，比现有方法提高了3.47%和4.04%。我们的代码和数据集可以在https://github.com/abcAnonymous/EDSL上找到。
英文摘要：
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 .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
