FROM STRUCTURAL SIMILARITY TO INFORMATIONFIDELITY: A NEW PARADIGM FOR EVALUATING TABLE RECOGNITION

The Hong Kong University of Science and Technology (Guangzhou)

Data Science and Analytics Thrust

PhD Qualifying Examination

By Mr LIU, Yuchen

Abstract

Accurate table recognition (TR) is a critical prerequisite for unlocking structured knowledge from the vast repositories of digital documents, a task of increasing importance in the era of Large Language Models. However, progress in the field is hindered by a fundamental flaw in its evaluation methodologies. Widely adopted metrics, such as Tree-Edit-Distance-based Similarity (TEDS) and Grid Table Similarity (GriTS), are designed to measure structural similarity. This review critically argues that structural similarity serves as an imperfect proxy for the true objective: the preservation of information fidelity. This discrepancy leads to a systemic “Fidelity Gap,” where models can achieve high evaluation scores while producing outputs with catastrophic, information-destroying errors, such as cell misalignments. To address this, we first provide a comprehensive survey of the architectural evolution of TR models, contextualizing the challenges of evaluation. We then conduct a deep, critical history of evaluation metrics, deconstructing the the oretical underpinnings of similarity-based approaches and revealing their inherent limitations. Finally, we propose a necessary paradigm shift towards directly quantifying information fidelity. We introduce a conceptual framework for this new paradigm and present the Table Fidelity Evaluation Metric (TFEM)—a concrete instantiation based on optimal transport—as a case study. Through extensive empirical evidence, including its stronger correlation with downstream task performance, we validate the superiority of the fidelity-oriented approach. This review advocates for the adoption of information fidelity as the new standard, providing a more accurate compass to guide the future development of truly trustworthy table recognition systems.

PQE Committee

Chair of Committee: Prof. DUAN, Lingjie（Online）

Prime Supervisor: Prof. CHEN, Lei (Online)

Co-Supervisor: Prof. LUO, Yuyu (Onsite)

Examiner: Prof. DING, Ningning (Onsite)