  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.
