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Paper Citation Record · LEDGER

Scene Text Recognition with Sliding Convolutional Character Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:1709.01727.

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

pith.paper-citation-record.v1
1709.01727 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 1 of 1 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T20:28:58.645516Z

measured 0 of 1 external citation measurements

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

Source: cited_works

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 ec2d7cb7-7569-40e7-8bc1-a9848774673e · inbound

Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning cites this paper.

Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning Scene Text Recognition with Sliding Convolutional Character Models

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-04T20:28:58.645516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T20:28:58.645516Z digest=sha256:a7015878fc37c07e7aeb894e1f51edf8e9ba10e4789499160db125c04363636e