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ARXIV:2604.02692 · DOCUMENT PARSING · SUBMITTED 06 APR · 20:16 UTC · FRESHNESS UNKNOWN
ARXIV:2604.02692DOCUMENT PARSINGSUBMITTED 06 APR · 20:16 UTCFRESHNESS UNKNOWNFuyuan Liu · Dianyu Yu · He Ren · Nayu Liu · Xiaomian Kang · Delai Qiu · +7 at arXiv
A structural refinement module stabilizes document parsing pipelines by ensuring consistent input order for downstream parsers, significantly reducing errors on complex layouts.
Opportunity summary
Pain A structural refinement module stabilizes document parsing pipelines by ensuring consistent input order for downstream parsers, significantly reducing errors on complex layouts.
Evidence 0 refs | 0 sources | 0% coverage
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A structural refinement module stabilizes document parsing pipelines by ensuring consistent input order for downstream parsers, significantly reducing errors on complex layouts. In explicit Document Layout Analysis (DLA) pipelines, downstream parsers do not consume…
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.
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Extensive experiments on public benchmarks show that our method consistently improves page-level layout quality. Code availability is flagged in the production record; the public…
Document Parsing moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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A structural refinement module stabilizes document parsing pipelines by ensuring consistent input order for downstream parsers, significantly reducing errors on complex layouts.
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10.48550/arXiv.2604.02692A structural refinement module stabilizes document parsing pipelines by ensuring consistent input order for downstream parsers, significantly reducing errors on complex layouts.
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.
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PROBLEM
A structural refinement module stabilizes document parsing pipelines by ensuring consistent input order for downstream parsers, significantly reducing errors on complex layouts. In explicit Document Layout Analysis (DLA) pipelines, downstream parsers do not consume the full dete...
METHOD
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.
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Extensive experiments on public benchmarks show that our method consistently improves page-level layout quality. Code availability is flagged in the production record; the public repository link still nee...
WHY NOW
Document Parsing moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A structural refinement module stabilizes document parsing pipelines by ensuring consistent input order for downstream parsers, significantly reducing errors on complex layouts. In explicit Document Layout Analysis (DLA) pipelines, downstream parsers do not consume the full detector output.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Extensive experiments on public benchmarks show that our method consistently improves page-level layout quality. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Document Parsing moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A structural refinement module stabilizes document parsing pipelines by ensuring consistent input order for downstream parsers, significantly reducing errors on complex layouts.
Segment
Document Parsing
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