  Table structure recognition (TSR) aims to convert tabular images into a
machine-readable format, where a visual encoder extracts image features and a
textual decoder generates table-representing tokens. Existing approaches use
classic convolutional neural network (CNN) backbones for the visual encoder and
transformers for the textual decoder. However, this hybrid CNN-Transformer
architecture introduces a complex visual encoder that accounts for nearly half
of the total model parameters, markedly reduces both training and inference
speed, and hinders the potential for self-supervised learning in TSR. In this
work, we design a lightweight visual encoder for TSR without sacrificing
expressive power. We discover that a convolutional stem can match classic CNN
backbone performance, with a much simpler model. The convolutional stem strikes
an optimal balance between two crucial factors for high-performance TSR: a
higher receptive field (RF) ratio and a longer sequence length. This allows it
to "see" an appropriate portion of the table and "store" the complex table
structure within sufficient context length for the subsequent transformer. We
conducted reproducible ablation studies and open-sourced our code at
https://github.com/poloclub/tsr-convstem to enhance transparency, inspire
innovations, and facilitate fair comparisons in our domain as tables are a
promising modality for representation learning.

Computer Vision and Pattern Recognition;Machine Learning