Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing

Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/boosting-document-parsing-efficiency-and-performance-with-coarse-to-fine-visual-processing

stale

Proof freshness: stale
Proof status: verified
Display score: 8/10
Last proof check: 2026-03-26
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID boosting-document-parsing-efficiency-and-performance-with-coarse-to-fine-visual-processing | Route /signal-canvas/boosting-document-parsing-efficiency-and-performance-with-coarse-to-fine-visual-processing

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/boosting-document-parsing-efficiency-and-performance-with-coarse-to-fine-visual-processing

MCP example

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    "paper_ref": "boosting-document-parsing-efficiency-and-performance-with-coarse-to-fine-visual-processing",
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  }
}

source_context

{
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  "mode": "paper",
  "query": "Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing",
  "normalized_query": "2603.24326",
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  "paper_ref": "boosting-document-parsing-efficiency-and-performance-with-coarse-to-fine-visual-processing",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing

PDF: https://arxiv.org/pdf/2603.24326v1

Repository: https://github.com/PaddlePaddle/PaddleOCR

Source count: Pending verification

Coverage: 50%

Last proof check: 2026-03-26T20:30:34.207Z

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Ready for execution: Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing

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Subject: Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing

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GitHub Code Pulse

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Stars

81,549

Health

Last commit

6/4/2026

Forks

10706

Open repository

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Extensive experiments demonstrate that PaddleOCR-VL achieves state-of-the-art performance in both page-level parsing and element-level recognition.
Implicationpartial
Explicitly stated in abstract with supporting benchmark results mentioned in analysis
Verificationpartial
partial
Evidencepartial
We propose PaddleOCR-VL, a novel coarse-to-fine architecture that focuses on semantically relevant regions while suppressing redundant ones, thereby improving both efficiency and performance.
Implicationpartial
Directly stated in abstract with clear technical explanation
Verificationpartial
partial
Evidencepartial
It significantly outperforms existing solutions... and delivers fast inference while utilizing substantially fewer vision tokens and parameters
Implicationpartial
Strongly supported in abstract and analysis with efficiency claims
Verificationpartial
partial
Evidencepartial
The model was evaluated on the OmniDocBench v1.5 benchmark, achieving state-of-the-art performance in text, formula, table, and reading order
Implicationpartial
Directly stated in analysis with specific benchmark details
Verificationpartial
partial
Evidencepartial
We design and train a compact yet powerful 0.9B vision-language model (PaddleOCR-VL-0.9B) to perform detailed recognition, guided by VRFM outputs
Implicationpartial
Specifically described in abstract with model size details
Verificationpartial
partial
Evidencepartial
Potential limitations include the model's dependency on the quality of the initial layout detection and the handling of highly anomalous document structures.
Implicationpartial
Explicitly stated as limitation in analysis section
Verificationpartial
partial
Evidencepartial
It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs
Implicationpartial
Directly stated in abstract but requires inference about comparison details
Verificationpartial
partial
Evidencepartial
While advanced research leveraging vision-language models benefits from high-resolution input to boost model performance, this often leads to a quadratic increase in the number of vision tokens and significantly raises computational costs.
Implicationpartial
Problem clearly described in abstract with solution implication
Verificationpartial
partial

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Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing

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