Paper Citation Record · LEDGER

Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.16809.

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

pith.paper-citation-record.v1
2310.16809 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 2026-08-19T06:32:44.657259+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:54:07.487795Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:16:02.407460Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 747fecb6-0335-46e2-92d3-675deca4df85 · inbound

Cross-Modal Consistency in Multimodal Large Language Models cites this paper.

Cross-Modal Consistency in Multimodal Large Language Models Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T20:54:07.487795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 843fe6c1-2ee9-4c8d-bfde-bae8a1831915 · inbound

MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs cites this paper.

MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation

Reference 175

Resolution: unresolved; no resolver link, observed 2026-08-12T14:31:37.419723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7997662e-8015-44a6-91df-eb6997ff828c · inbound

Early evidence of how LLMs outperform traditional systems on OCR/HTR tasks for historical records cites this paper.

Early evidence of how LLMs outperform traditional systems on OCR/HTR tasks for historical records Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T18:06:38.176758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fdf8dd7-3e1b-4abe-8d12-d73a5afec025 · inbound

Exploring Light-Weight Object Recognition for Real-Time Document Detection cites this paper.

Exploring Light-Weight Object Recognition for Real-Time Document Detection Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T23:55:07.168842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3799812-2777-4fde-85c3-892f684b2402 · inbound

From Handwriting to Structured Data: Benchmarking AI Digitisation of Handwritten Forms cites this paper.

From Handwriting to Structured Data: Benchmarking AI Digitisation of Handwritten Forms Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:16:02.448353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dc601ed0-71f4-416a-a89a-46f156c7a3b3 · inbound

Visual Information Extraction from Documents via Classification-Guided Large Vision-Language Models cites this paper.

Visual Information Extraction from Documents via Classification-Guided Large Vision-Language Models Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T11:59:18.087177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.