Paper Citation Record · LEDGER

Anonymization of Documents for Law Enforcement with Machine Learning

As of 20 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2501.07334.

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

pith.paper-citation-record.v1
2501.07334 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:46:45.585388Z

measured 34 of 34 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact1
verified fuzzy32
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bce62af7-847f-499c-b92a-60183489e3d5 · outbound

This paper cites Available: http://data.europa.eu/eli/reg/2016/679/oj/eng.

Anonymization of Documents for Law Enforcement with Machine Learning Available: http://data.europa.eu/eli/reg/2016/679/oj/eng

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.329082Z

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 0537ba5d-7fd9-459f-99eb-475a5e05a9b8 · outbound

This paper cites California Consumer Privacy Act of 2018.

Anonymization of Documents for Law Enforcement with Machine Learning California Consumer Privacy Act of 2018

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.310798Z

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 8fc262a6-120b-4e97-9f06-f5d09dc8e89e · outbound

This paper cites Privacy Preserving by Removing Sensitive Data from Documents with Fully Convolutional Networks,.

Anonymization of Documents for Law Enforcement with Machine Learning Privacy Preserving by Removing Sensitive Data from Documents with Fully Convolutional Networks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.293039Z

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 10603ecf-4718-4a52-8dae-91534c80fe64 · outbound

This paper cites Anonymization of German financial documents using neural network-based language models with contextual word representations,.

Anonymization of Documents for Law Enforcement with Machine Learning Anonymization of German financial documents using neural network-based language models with contextual word representations,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.271461Z

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 b42a90bf-d22d-4a78-99d1-80022dd609e1 · outbound

This paper cites AGORA: An intelligent system for the anonymization, information extraction and automatic mapping of sensitive documents,.

Anonymization of Documents for Law Enforcement with Machine Learning AGORA: An intelligent system for the anonymization, information extraction and automatic mapping of sensitive documents,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.249870Z

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 322ed913-79fc-471f-8f68-667d68ac49f9 · outbound

This paper cites Document anonymization for border guards and immigration services,.

Anonymization of Documents for Law Enforcement with Machine Learning Document anonymization for border guards and immigration services,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.224881Z

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 5d74bf2b-140f-4993-b169-0c7285cb6392 · outbound

This paper cites Federated tool for anonymization and annotation in image data,.

Anonymization of Documents for Law Enforcement with Machine Learning Federated tool for anonymization and annotation in image data,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.199828Z

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 7f6da302-8a44-48ad-96d9-43563ab38f70 · outbound

This paper cites Ultralyt- ics/yolov5: V7.0 - YOLOv5 SOTA Realtime Instance Segmentation,.

Anonymization of Documents for Law Enforcement with Machine Learning Ultralyt- ics/yolov5: V7.0 - YOLOv5 SOTA Realtime Instance Segmentation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.178762Z

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 49d48f72-3b4f-4e4e-b568-301136af213c · outbound

This paper cites Scalable logo recognition in real-world images,.

Anonymization of Documents for Law Enforcement with Machine Learning Scalable logo recognition in real-world images,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.155579Z

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 ab1e2e8f-29d9-415e-84b6-85883a229f0c · outbound

This paper cites Automatic Anonymization of Printed-Text Document Images,.

Anonymization of Documents for Law Enforcement with Machine Learning Automatic Anonymization of Printed-Text Document Images,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.132860Z

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 503ad24f-abfe-4ad3-8221-18d01f54bd77 · outbound

This paper cites Applications of Machine Learning in Digital Forensics,.

Anonymization of Documents for Law Enforcement with Machine Learning Applications of Machine Learning in Digital Forensics,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.112587Z

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 4c471445-dde7-4a0c-b88e-fb1467805609 · outbound

This paper cites Digital forensics supported by machine learning for the detection of online sexual predatory chats,.

Anonymization of Documents for Law Enforcement with Machine Learning Digital forensics supported by machine learning for the detection of online sexual predatory chats,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.094827Z

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 dbcb81f3-1b70-4027-b06d-0dc3dd35f341 · outbound

This paper cites Deep Learning for Person Re-Identification: A Survey and Outlook,.

Anonymization of Documents for Law Enforcement with Machine Learning Deep Learning for Person Re-Identification: A Survey and Outlook,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.074135Z

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 0ea8c57c-b979-42cb-a328-a2ab6ac689d9 · outbound

This paper cites Spatial-Temporal Person Re-Identification,.

Anonymization of Documents for Law Enforcement with Machine Learning Spatial-Temporal Person Re-Identification,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.052688Z

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 36f79143-d4fd-4165-95ac-99f79ca4e076 · outbound

This paper cites How im- portant are faces for person re-identification?.

Anonymization of Documents for Law Enforcement with Machine Learning How im- portant are faces for person re-identification?

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.030162Z

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 2d0de4ea-2ba6-4200-9c33-1a84f1849eda · outbound

This paper cites Printer Identification Methods Using Global and Local Feature-Based Deep Learning,.

Anonymization of Documents for Law Enforcement with Machine Learning Printer Identification Methods Using Global and Local Feature-Based Deep Learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:46.011543Z

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 71d2f6b6-9f67-4437-ab85-a9c15bc5ce98 · outbound

This paper cites Printer source identification of quick response codes using residual attention network and smartphones,.

Anonymization of Documents for Law Enforcement with Machine Learning Printer source identification of quick response codes using residual attention network and smartphones,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.992873Z

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 a10f4115-9843-4ad5-baca-84d86f3f9b56 · outbound

This paper cites Clas- sification of Inkjet Printers based on Droplet Statistics,.

Anonymization of Documents for Law Enforcement with Machine Learning Clas- sification of Inkjet Printers based on Droplet Statistics,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.972511Z

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 572f806b-cc59-4a65-b71e-4cc4e6644e40 · outbound

This paper cites Deep Learning for Instance Retrieval: A Survey,.

Anonymization of Documents for Law Enforcement with Machine Learning Deep Learning for Instance Retrieval: A Survey,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.951865Z

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 271e70b3-4d11-4f68-be56-8c51b6452f51 · outbound

This paper cites DINOv2: Learning Robust Vi- sual Features without Supervision,.

Anonymization of Documents for Law Enforcement with Machine Learning DINOv2: Learning Robust Vi- sual Features without Supervision,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.931728Z

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 bb02ae6c-c774-45ac-9a38-d3bc64a1fb8f · outbound

This paper cites An Image is Worth 16x16 Words: Trans- formers for Image Recognition at Scale,.

Anonymization of Documents for Law Enforcement with Machine Learning An Image is Worth 16x16 Words: Trans- formers for Image Recognition at Scale,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.910144Z

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 a7332df0-a008-4389-b3b3-3f6ade799ae9 · outbound

This paper cites Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces,.

Anonymization of Documents for Law Enforcement with Machine Learning Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.884580Z

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 6b28658b-4f72-47c9-b844-74d0f50e7d1d · outbound

This paper cites Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,.

Anonymization of Documents for Law Enforcement with Machine Learning Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.866373Z

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 0dfe9417-329c-4f1c-b2b4-4411df3bbfff · outbound

This paper cites YuNet: A Tiny Millisecond-level Face Detector,.

Anonymization of Documents for Law Enforcement with Machine Learning YuNet: A Tiny Millisecond-level Face Detector,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.845991Z

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 a57da62b-b584-44cd-b113-131355726233 · outbound

This paper cites PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR System,.

Anonymization of Documents for Law Enforcement with Machine Learning PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR System,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.821145Z

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 216ff48b-0319-4f83-b40a-a5383be26d54 · outbound

This paper cites PP-OCRv2: Bag of Tricks for Ultra Lightweight OCR System,.

Anonymization of Documents for Law Enforcement with Machine Learning PP-OCRv2: Bag of Tricks for Ultra Lightweight OCR System,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.800720Z

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 84f7f9a8-413f-46ed-b963-b7320c6dc538 · outbound

This paper cites Detecting machine-readable zones in passport images,.

Anonymization of Documents for Law Enforcement with Machine Learning Detecting machine-readable zones in passport images,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.782979Z

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 033722b3-6a5e-4d28-b1db-68c55ad24615 · outbound

This paper cites Optimal Filters for Extended Optical Flow,.

Anonymization of Documents for Law Enforcement with Machine Learning Optimal Filters for Extended Optical Flow,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.763908Z

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 04572ec4-f338-4e18-9e5c-bf5aa4120e1e · outbound

This paper cites Konstantint/PassportEye,.

Anonymization of Documents for Law Enforcement with Machine Learning Konstantint/PassportEye,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.744167Z

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 227a95ed-bb5e-4e5f-b228-58a74e1100d3 · outbound

This paper cites Ultralytics Signature Detection Dataset,.

Anonymization of Documents for Law Enforcement with Machine Learning Ultralytics Signature Detection Dataset,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.721530Z

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 70f2adb3-4072-43e9-a6ae-b8ac02156b12 · outbound

This paper cites Offline Sig- nature Verification on Real-World Documents,.

Anonymization of Documents for Law Enforcement with Machine Learning Offline Sig- nature Verification on Real-World Documents,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.702745Z

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 335dc672-3265-45f6-8624-997fd8d76d48 · outbound

This paper cites Stepan-coder/HandWritenSignatureDetection,.

Anonymization of Documents for Law Enforcement with Machine Learning Stepan-coder/HandWritenSignatureDetection,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:46:45.685581Z

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 feddf225-c3d6-4ddb-8fa5-d90415c06e0f · outbound

This paper cites AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2.

Anonymization of Documents for Law Enforcement with Machine Learning AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:46:45.667054Z

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 846e8ca6-fef4-45b1-b38a-7649fe6f80e3 · outbound

This paper cites General Purpose Image Encoder DINOv2 for Medical Image Registration.

Anonymization of Documents for Law Enforcement with Machine Learning General Purpose Image Encoder DINOv2 for Medical Image Registration

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T20:46:45.585388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.