Parser-Oriented Structural Refinement for a Stable Layout Interface in Document Parsing

2026-04-02

AI Summary

This work addresses severe parsing errors in dense document pages caused by unstable layout assumptions, which lead to mismatches between detector outputs and the input sequence expected by the parser. To resolve this, the authors introduce a lightweight structural refinement module between a DETR-style detector and the parser, performing set-level reasoning using query features, semantic cues, bounding box geometry, and visual evidence. This module jointly decides which instances to retain, refines bounding boxes, and predicts the correct parsing order. By integrating retention-oriented supervision with a difficulty-aware ordering objective, the method significantly enhances layout–parsing interface consistency under complex layouts, reducing the reading order edit distance to 0.024 on OmniDocBench while consistently improving page-level layout quality.

Abstract

Accurate document parsing requires both robust content recognition and a stable parser interface. In explicit Document Layout Analysis (DLA) pipelines, downstream parsers do not consume the full detector output. Instead, they operate on a retained and serialized set of layout instances. However, on dense pages with overlapping regions and ambiguous boundaries, unstable layout hypotheses can make the retained instance set inconsistent with its parser input order, leading to severe downstream parsing errors. To address this issue, we introduce a lightweight structural refinement stage between a DETR-style detector and the parser to stabilize the parser interface. Treating raw detector outputs as a compact hypothesis pool, the proposed module performs set-level reasoning over query features, semantic cues, box geometry, and visual evidence. From a shared refined structural state, it jointly determines instance retention, refines box localization, and predicts parser input order before handoff. We further introduce retention-oriented supervision and a difficulty-aware ordering objective to better align the retained instance set and its order with the final parser input, especially on structurally complex pages. Extensive experiments on public benchmarks show that our method consistently improves page-level layout quality. When integrated into a standard end-to-end parsing pipeline, the stabilized parser interface also substantially reduces sequence mismatch, achieving a Reading Order Edit of 0.024 on OmniDocBench.

Problem

• document parsing
• layout analysis
• parser interface stability
• instance retention
• reading order

Innovation

• structural refinement
• parser interface stabilization
• document layout analysis
• instance retention
• reading order prediction