论文标题
使用对抗性学习和变形金刚的离线手写数学识别
Offline Handwritten Mathematical Recognition using Adversarial Learning and Transformers
论文作者
论文摘要
离线手写数学表达识别（HMER）是数学表达识别领域的主要领域。由于缺乏时间信息和写作风格的可变性，与在线HMER相比，离线HMER通常被认为是一个要困难得多的问题。在本文中，我们目的是使用配对对手学习的编码器模型。语义不变的特征是从手写数学表达图像及其编码器中的印刷数学表达式中提取的。学习语义不变的特征与登音编码器和变压器解码器相结合，帮助我们提高了先前研究的表达速率。在Crohme数据集上进行了评估，我们能够将最新的Crohme 2019测试集结果提高4％。
Offline Handwritten Mathematical Expression Recognition (HMER) is a major area in the field of mathematical expression recognition. Offline HMER is often viewed as a much harder problem as compared to online HMER due to a lack of temporal information and variability in writing style. In this paper, we purpose a encoder-decoder model that uses paired adversarial learning. Semantic-invariant features are extracted from handwritten mathematical expression images and their printed mathematical expression counterpart in the encoder. Learning of semantic-invariant features combined with the DenseNet encoder and transformer decoder, helped us to improve the expression rate from previous studies. Evaluated on the CROHME dataset, we have been able to improve latest CROHME 2019 test set results by 4% approx.
