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.

本文提出一种高性能的手写数学表达式识别模型，采用比例增强和 drop attention 技术解决了二维结构数学表达式识别中尺度不稳定的问题，并使用基于注意力机制的编码器 - 解码器网络从中提取特征并生成预测结果。我们的方法在 CROHME 2014 和 CROHME 2016 两个公共数据集上取得了最先进的性能。

利用比例增强和 Drop Attention 提高基于注意力的手写数学表达式识别

Improving Attention-Based Handwritten Mathematical Expression  Recognition with Scale Augmentation and Drop Attention

We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR)
model. We learn to listen and write characters with a joint Connectionist
Temporal Classification (CTC) and attention-based encoder-decoder network. The
encoder is a deep Convolutional Neural Network (CNN) based on the VGG network.
The CTC network sits on top of the encoder and is jointly trained with the
attention-based decoder. During the beam search process, we combine the CTC
predictions, the attention-based decoder predictions and a separately trained
LSTM language model. We achieve a 5-10\% error reduction compared to prior
systems on spontaneous Japanese and Chinese speech, and our end-to-end model
beats out traditional hybrid ASR systems.

本研究提出了一个最先进的端到端自动语音识别模型，通过使用联合 CTC 和基于注意力机制的编码解码器网络来学习听和写字，其中编码器是基于 VGG 网络的深度 CNN，CTC 网络和注意力解码器共同训练，通过在波束搜索过程中，将 CTC 预测、注意力解码器预测和单独训练的 LSTM 语言模型相结合，相较于先前的系统，在自发性日语和中文语音上减少了 5-10% 的误差，并且我们的端到端模型击败了传统的混合式 ASR 系统。