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

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.

Figures (3)

• Figure 1: Illustration of detector-to-parser handoff failures on a page. A dense page may produce multiple overlapping layout candidates around the same content region. Without suppression, duplicated candidates may be passed to the parser, causing content redundancy and an incorrect reading sequence. With heuristic NMS, an incomplete survivor may be retained while a better-localized alternative is removed, leading to content loss and an unstable parser input.
• Figure 2: Overview of the proposed parser-oriented structural refinement framework. Given a document image, a DETR-style detector first produces a compact pool of layout hypotheses. A lightweight refinement module then performs set-level reasoning over detector query features, semantic cues, box geometry, and image evidence, so that localization, instance retention, and parser input order are determined coherently from the same refined state. The downstream OCR-VL parser consumes a clean and well-ordered layout set, yielding more faithful Markdown/JSON output, particularly on structurally challenging pages.
• Figure 3: Qualitative comparison on two challenging layouts (Case A: equations, Case B: chemical schemes). Our method avoids the severe redundancy of raw outputs and the content loss caused by traditional NMS, yielding clean and complete structures.