TRACE: Table Reconstruction Aligned to Corner and Edges

• URL: http://arxiv.org/abs/2305.00630v1
• Date: Mon, 1 May 2023 02:26:15 GMT
• Title: TRACE: Table Reconstruction Aligned to Corner and Edges
• Authors: Youngmin Baek, Daehyun Nam, Jaeheung Surh, Seung Shin, Seonghyeon Kim
• Abstract summary: We analyze the natural characteristics of a table, where a table is composed of cells and each cell is made up of borders consisting of edges. We propose a novel method to reconstruct the table in a bottom-up manner. A simple design makes the model easier to train and requires less computation than previous two-stage methods.
• Score: 7.536220920052911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A table is an object that captures structured and informative content within a document, and recognizing a table in an image is challenging due to the complexity and variety of table layouts. Many previous works typically adopt a two-stage approach; (1) Table detection(TD) localizes the table region in an image and (2) Table Structure Recognition(TSR) identifies row- and column-wise adjacency relations between the cells. The use of a two-stage approach often entails the consequences of error propagation between the modules and raises training and inference inefficiency. In this work, we analyze the natural characteristics of a table, where a table is composed of cells and each cell is made up of borders consisting of edges. We propose a novel method to reconstruct the table in a bottom-up manner. Through a simple process, the proposed method separates cell boundaries from low-level features, such as corners and edges, and localizes table positions by combining the cells. A simple design makes the model easier to train and requires less computation than previous two-stage methods. We achieve state-of-the-art performance on the ICDAR2013 table competition benchmark and Wired Table in the Wild(WTW) dataset.

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