Abstract
The use of artificial intelligence technology in education is growing rapidly, with increasing attention being paid to handwritten mathematical expression recognition (HMER) by researchers. However, many existing methods for HMER may fail to accurately read formulas with complex structures, as the attention results can be inaccurate due to illegible handwriting or large variations in writing styles. Our proposed Intelligent-Detection Network (IDN) for HMER differs from traditional encoder-decoder methods by utilizing object detection techniques. Specifically, we have developed an enhanced YOLOv7 network that can accurately detect both digital and symbolic objects. The detection results are then integrated into the bidirectional gated recurrent unit (BiGRU) and the baseline symbol relationship tree (BSRT) to determine the relationships between symbols and numbers. The experiments demonstrate that the proposed method outperforms those encoder-decoder networks in recognizing complex handwritten mathematical expressions. This is due to the precise detection of symbols and numbers. Our research has the potential to make valuable contributions to the field of HMER. This could be applied in various practical scenarios, such as assignment grading in schools and information entry of paper documents.
Abstract (translated)
人工智能技术在教育领域的应用迅速增长，研究人员越来越关注手写数学表达识别（HMER）。然而，许多现有方法可能无法准确地识别具有复杂结构的复杂数学公式，因为注意结果可能因不清晰的手写或写作风格的巨大差异而出现误差。我们提出的智能检测网络（IDN） for HMER与传统编码器-解码器方法不同，因为它利用了物体检测技术。具体来说，我们开发了一个增强的 YOLOv7 网络，可以准确检测数字和符号物体。检测结果 then 集成到双向门控循环单元（BiGRU）和基线符号关系树（BSRT）中，以确定符号和数字之间的关系。实验证明，与传统的编码器-解码器网络相比，该方法在识别复杂手写数学表达式方面表现出优异性能。这是由于符号和数字的准确检测。我们的研究有望为 H梅尔领域做出有价值的贡献。这可以在各种实际场景中应用，例如学校中的作业评分和纸质文件的信息录入等。
URL
https://arxiv.org/abs/2311.15273
