Rethinking Image-based Table Recognition Using Weakly Supervised Methods

Understand

Most of the previous methods for table recognition rely on training datasets containing many richly annotated table images.

• Detailed table image annotation, e.g., cell or text bounding box annotation, however, is costly and often subjective.
• In this paper, we propose a weakly supervised model named WSTabNet for table recognition that relies only on HTML (or LaTeX) code-level annotations of table images.
• The proposed model consists of three main parts: an encoder for feature extraction, a structure decoder for generating table structure, and a cell decoder for predicting the content of each cell in the table.