Paper Citation Record · LEDGER

Deep Learning based Visually Rich Document Content Understanding: A Survey

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2408.01287.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2408.01287 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:45:05.938717Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation aedf4986-3e8a-4395-982f-8252b9bd0f4d · inbound

Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends cites this paper.

Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends Deep Learning based Visually Rich Document Content Understanding: A Survey

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T22:17:27.543243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11e8bc94-7020-4396-a36f-4951c7d3c574 · inbound

Hierarchical Document Parsing via Large Margin Feature Matching and Heuristics cites this paper.

Hierarchical Document Parsing via Large Margin Feature Matching and Heuristics Deep Learning based Visually Rich Document Content Understanding: A Survey

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T12:49:28.254909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdd6386d-2962-45d7-a728-e6182fe73477 · inbound

jina-embeddings-v4: Universal Embeddings for Multimodal Multilingual Retrieval cites this paper.

jina-embeddings-v4: Universal Embeddings for Multimodal Multilingual Retrieval Deep Learning based Visually Rich Document Content Understanding: A Survey

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T18:45:05.938717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16e87962-239c-4b4c-888d-6c0db01ac3e6 · inbound

A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends cites this paper.

A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends Deep Learning based Visually Rich Document Content Understanding: A Survey

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-19T04:42:04.036736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a577dc6c-28e4-4526-bf59-61180c98fc14 · inbound

Multi-Modal Vision vs. Text-Based Parsing: Benchmarking LLM Strategies for Invoice Processing cites this paper.

Multi-Modal Vision vs. Text-Based Parsing: Benchmarking LLM Strategies for Invoice Processing Deep Learning based Visually Rich Document Content Understanding: A Survey

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T14:20:23.881572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6bb7428-af19-4fc2-bf0f-0445c4baed10 · inbound

DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth cites this paper.

DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth Deep Learning based Visually Rich Document Content Understanding: A Survey

Reference 226

Resolution: unresolved; no resolver link, observed 2026-08-02T14:55:43.368348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.