TRACE: Table Reconstruction Aligned to Corner and Edges
Youngmin Baek, Daehyun Nam, Jaeheung Surh, Seung Shin, Seonghyeon Kim

TL;DR
This paper introduces TRACE, a bottom-up table reconstruction method that directly localizes table cells using corners and edges, reducing complexity and error propagation compared to traditional two-stage approaches.
Contribution
The novel bottom-up approach reconstructs tables from low-level features, simplifying training and inference while achieving state-of-the-art results.
Findings
Achieves state-of-the-art performance on ICDAR2013 benchmark.
Requires less computation than previous methods.
Simplifies table reconstruction by focusing on cell borders.
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…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Image Retrieval and Classification Techniques · Image Processing and 3D Reconstruction
