Cited by 10

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Leveraging effectiveness and efficiency in Page Stream Deep Segmentation

Braz, Fabricio Ataides, da Silva, Nilton Correia, Lima, Jonathan Alis Salgado·2021·Engineering Applications of Artificial Intelligence

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.engappai.2021.104394

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Research works printed but not linked: 10

  1. Agin, 2015, An approach to the segmentation of multi-page document flow using binary classification, vol. 9443
  2. Altan, 2020, Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique, Chaos Solitons Fractals, 140, 10.1016/j.chaos.2020.110071 · DOI
  3. Daher, 2014, Document flow segmentation for business applications, vol. 9021, 90210G
  4. Ferri, 2009, An experimental comparison of performance measures for classification, Pattern Recognit. Lett., 30, 27, 10.1016/j.patrec.2008.08.010 · DOI · Paper page
  5. Harley, A.W., Ufkes, A., Derpanis, K.G., 2015. Evaluation of deep convolutional nets for document image classification and retrieval. In: International Conference on Document Analysis and Recognition. ICDAR. · DOI
  6. Isemann, D., Niekler, A., Preßler, B., Viereck, F., Heyer, G., 2014. OCR of legacy documents as a building block in industrial disaster prevention. In: DIMPLE Workshop on DIsaster Management and Principled Large-Scale Information Extraction for and Post Emergency Logistics. LREC 2014.
  7. Neche, C., Belaïd, Y., Belaïd, A., 2020. Use of language models for document stream segmentation. In: International Conference on Pattern Recognition Applications and Methods. · DOI · Paper page
  8. Pes, 2020, Learning from high-dimensional biomedical datasets: The issue of class imbalance, IEEE Access, 8, 13527, 10.1109/ACCESS.2020.2966296 · DOI
  9. Zhu, G., Doermann, D., 2007. Automatic document logo detection. In: Proc. 9th International Conf. Document Analysis and Recognition. ICDAR 2007. pp. 864–868. · DOI
  10. Zhu, G., Zheng, Y., Doermann, D., Jaeger, S., 2007. Multi-scale structural saliency for signature detection. In: In Proc. IEEE Conf. Computer Vision and Pattern Recognition. CVPR 2007. pp. 1–8. · DOI

Metadata

Authors
Braz, F. A., da Silva, N. C., & Lima, J. A. S.
Venue
Engineering Applications of Artificial Intelligence
Publisher
Elsevier BV
Published
2021-10
Volume
105
Pages
104394
ISSN
0952-1976
Cited by
10
References
2
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Leveraging effectiveness and efficiency in Page Stream Deep Segmentation. https://app.cochat.ai/papers/10.1016/j.engappai.2021.104394
The paper
Braz, F. A., da Silva, N. C., & Lima, J. A. S. (2021). Leveraging effectiveness and efficiency in Page Stream Deep Segmentation. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2021.104394