论文标题
使用多尺度强大的神经网络识别手写的数学表达式为乳胶序列
Recognizing Handwritten Mathematical Expressions as LaTex Sequences Using a Multiscale Robust Neural Network
论文作者
论文摘要
在本文中，提出了强大的多尺度神经网络来识别手写数学表达式和输出乳胶序列，该序列可以有效，正确地关注输出的每个步骤应关注的每个步骤，并对分析手写数学表达式的二维结构产生积极影响，并在长表达中识别不同的数学符号。随着可视化的添加，详细显示了模型的识别过程。此外，我们的模型在公共Crohme 2014和Crohme 2016数据集中获得了49.459％和46.062％的征收。目前的模型结果表明，最新模型具有更好的鲁棒性，更少的错误和更高的准确性。
In this paper, a robust multiscale neural network is proposed to recognize handwritten mathematical expressions and output LaTeX sequences, which can effectively and correctly focus on where each step of output should be concerned and has a positive effect on analyzing the two-dimensional structure of handwritten mathematical expressions and identifying different mathematical symbols in a long expression. With the addition of visualization, the model's recognition process is shown in detail. In addition, our model achieved 49.459% and 46.062% ExpRate on the public CROHME 2014 and CROHME 2016 datasets. The present model results suggest that the state-of-the-art model has better robustness, fewer errors, and higher accuracy.
