Despite the recent advances in optical character recognition (OCR),
mathematical expressions still face a great challenge to recognize due to their
two-dimensional graphical layout. In this paper, we propose a convolutional
sequence modeling network, ConvMath, which converts the mathematical expression
description in an image into a LaTeX sequence in an end-to-end way. The network
combines an image encoder for feature extraction and a convolutional decoder
for sequence generation. Compared with other Long Short Term Memory(LSTM) based
encoder-decoder models, ConvMath is entirely based on convolution, thus it is
easy to perform parallel computation. Besides, the network adopts multi-layer
attention mechanism in the decoder, which allows the model to align output
symbols with source feature vectors automatically, and alleviates the problem
of lacking coverage while training the model. The performance of ConvMath is
evaluated on an open dataset named IM2LATEX-100K, including 103556 samples. The
experimental results demonstrate that the proposed network achieves
state-of-the-art accuracy and much better efficiency than previous methods.

本文提出一种称为 ConvMath 的卷积序列建模网络，可将图像中的数学表达式描述转换为 LaTeX 序列，并通过多层注意机制和卷积解码器来提高模型的准确性和效率。在 IM2LATEX-100K 数据集上的实验结果表明，该网络实现了最先进的准确度和比以前方法更好的效率。