论文标题
通过扩大量表提高基于注意力的手写数学表达识别并放弃注意力
Improving Attention-Based Handwritten Mathematical Expression Recognition with Scale Augmentation and Drop Attention
论文作者
论文摘要
手写数学表达识别（HMER）是手写识别的重要研究方向。 HMER的性能受到数学表达式（MES）的二维结构。为了解决这个问题，在本文中，我们提出了一个具有尺度增强并放弃注意力的高性能HMER模型。具体而言，在水平和垂直方向上以不稳定的尺度来解决我，比例扩大可以提高模型在各种尺度的MES上的性能。基于注意力的编码器 - 编码器网络用于提取特征和生成预测。此外，提出了降低的注意，以进一步提高解码器的注意力分布。与以前的方法相比，我们的方法在Crohme 2014和Crohme 2016的两个公共数据集上实现了最先进的性能。
Handwritten mathematical expression recognition (HMER) is an important research direction in handwriting recognition. The performance of HMER suffers from the two-dimensional structure of mathematical expressions (MEs). To address this issue, in this paper, we propose a high-performance HMER model with scale augmentation and drop attention. Specifically, tackling ME with unstable scale in both horizontal and vertical directions, scale augmentation improves the performance of the model on MEs of various scales. An attention-based encoder-decoder network is used for extracting features and generating predictions. In addition, drop attention is proposed to further improve performance when the attention distribution of the decoder is not precise. Compared with previous methods, our method achieves state-of-the-art performance on two public datasets of CROHME 2014 and CROHME 2016.
