Tables organize valuable content in a concise and compact representation.
This content is extremely valuable for systems such as search engines,
Knowledge Graph's, etc, since they enhance their predictive capabilities.
Unfortunately, tables come in a large variety of shapes and sizes. Furthermore,
they can have complex column/row-header configurations, multiline rows,
different variety of separation lines, missing entries, etc. As such, the
correct identification of the table-structure from an image is a non-trivial
task. In this paper, we present a new table-structure identification model. The
latter improves the latest end-to-end deep learning model (i.e.
encoder-dual-decoder from PubTabNet) in two significant ways. First, we
introduce a new object detection decoder for table-cells. In this way, we can
obtain the content of the table-cells from programmatic PDF's directly from the
PDF source and avoid the training of the custom OCR decoders. This
architectural change leads to more accurate table-content extraction and allows
us to tackle non-english tables. Second, we replace the LSTM decoders with
transformer based decoders. This upgrade improves significantly the previous
state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple
tables and from 88.7% to 95% on complex tables.

本文提出了一种新的表格结构识别模型，通过引入新的对象检测解码器和基于 transformer 的解码器，能够更准确地提取表格内容并处理各种形式的表格。