TFLOP: Table Structure Recognition Framework with Layout Pointer Mechanism
Minsoo Khang, Teakgyu Hong

TL;DR
TFLOP is a novel table structure recognition framework that directly predicts text regions using a layout pointer mechanism, improving accuracy and efficiency over previous methods, especially in complex and industrial scenarios.
Contribution
The paper introduces TFLOP, a new TSR framework that reformulates text region prediction as a direct pointing task, eliminating the need for region matching and alignment.
Findings
Achieves state-of-the-art results on PubTabNet, FinTabNet, and SynthTabNet.
Performs well on industrial documents with watermarks and non-English content.
Enhances table recognition accuracy with span-aware contrastive supervision.
Abstract
Table Structure Recognition (TSR) is a task aimed at converting table images into a machine-readable format (e.g. HTML), to facilitate other applications such as information retrieval. Recent works tackle this problem by identifying the HTML tags and text regions, where the latter is used for text extraction from the table document. These works however, suffer from misalignment issues when mapping text into the identified text regions. In this paper, we introduce a new TSR framework, called TFLOP (TSR Framework with LayOut Pointer mechanism), which reformulates the conventional text region prediction and matching into a direct text region pointing problem. Specifically, TFLOP utilizes text region information to identify both the table's structure tags and its aligned text regions, simultaneously. Without the need for region prediction and alignment, TFLOP circumvents the additional text…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsWeb Data Mining and Analysis · Advanced Database Systems and Queries · Data Quality and Management
