CoChat
Sign Up
Cited by 19

The DOI matches its Crossref record; the references it cites are not checked yet.

Multi-modal page stream segmentation with convolutional neural networks

Wiedemann, Gregor, Heyer, Gerhard·2021·Language Resources and Evaluation
DOI 10.1007/s10579-019-09476-2
Open this paper in CoChat — free account

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 · 10.1007/s10579-019-09476-2

Cites

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

The citation lists haven’t been fetched yet

Cited by 19

Research works printed but not linked: 30

  1. Agin, O., Ulas, C., Ahat, M., & Bekar, C. (2015). An approach to the segmentation of multi-page document flow using binary classification. In Proceedings of the 6th international conference on graphic and image processing, https://doi.org/10.1117/12.2178778. · DOI · Paper page
  2. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. URL http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf.
  3. Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5, 135–146. · DOI · Paper page
  4. Cho, K., Van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder–decoder for statistical machine translation. In Proceedings of the 2014 conference on empirical methods in natural language processing (pp. 1724–1734). Association for Computational Linguistics, https://doi.org/10.3115/v1/D14-1179. URL http://aclweb.org/anthology/D14-1179. · DOI · Paper page
  5. Daher, H., & Belaïd (2014). Document flow segmentation for business applications. In Proceedings of document recognition and retrieval XXI (pp. 9201–9215). San Francisco, France, URL https://hal.archives-ouvertes.fr/hal-00926615.
  6. Daher, H., Bouguelia, M. R., Belaid, A., & DAndecy, V. P. (2014). Multipage administrative document stream segmentation. In Proceedings of the 22nd International Conference on Pattern Recognition (pp. 966–971). https://doi.org/10.1109/ICPR.2014.176. · DOI · Paper page
  7. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391–407. · DOI
  8. Fan, R. E., Chang, K. W., Hsieh, C. J., Wang, X. R., & Lin, C. J. (2008). LIBLINEAR: a library for large linear classification. Journal of Machine Learning Research, 9, 1871–1874. URL http://jmlr.org/papers/volume9/fan08a/fan08a.pdf.
  9. Gallo, I., Noce, L., Zamberletti, A., & Calefati, A. (2016). Deep neural networks for page stream segmentation and classification. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (pp 1–7), 2016. https://doi.org/10.1109/DICTA.2016.7797031. · DOI
  10. Gordo, A., Rusinol, M., Karatzas, D., & Bagdanov, A. D. (2013). Document classification and page stream segmentation for digital mailroom applications. In Proceedings of the 12th International Conference on Document Analysis and Recognition (pp 621–625). https://doi.org/10.1109/ICDAR.2013.128. · DOI · Paper page
  11. Hamdi, A., Voerman, J., Coustaty, M., Joseph, A., d’Andecy, V. P., & Ogier, J. M. (2017). Machine learning vs deterministic rule-based system for document stream segmentation. In Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) (pp 77–82). https://doi.org/10.1109/ICDAR.2017.332. · DOI · Paper page
  12. Hamdi, A., Coustaty, M., Joseph, A., d’Andecy, V. P., Doucet, A., & Ogier, J. M. (2018). Feature selection for document flow segmentation. In Proceedings of the 13th IAPR International Workshop on Document Analysis Systems (pp 245–250). https://doi.org/10.1109/DAS.2018.66. · DOI · Paper page
  13. Harley, A. W., Ufkes, A., & Derpanis, K. G. (2015). Evaluation of deep convolutional nets for document image classification and retrieval. In Proceedings of the 13th International Conference on Document Analysis and Recognition (ICDAR) (pp. 991–995). https://doi.org/10.1109/ICDAR.2015.7333910. · DOI
  14. Isemann, D., Niekler, A., Preßler, B., Viereck, F., & Heyer, G. (2014). OCR of legacy documents as a building block in industrial disaster prevention. In Proceedings of the DIMPLE@LREC Workshop on Disaster Management and Principled Large-scale information Extraction for and post emergency logistics
  15. Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning (pp. 137–142). Berlin: Springer. ISBN 978-3-540-69781-7.
  16. Karpinski, R., & Belaïd, A. (2016). Combination of structural and factual descriptors for document stream segmentation. In Proceedings of the 12th IAPR Workshop on Document Analysis Systems (pp. 221–226). https://doi.org/10.1109/DAS.2016.21. · DOI · Paper page
  17. Kim, Y. (2014). Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (pp. 1746–1751). Association for Computational Linguistics, https://doi.org/10.3115/v1/D14-1181. URL http://aclweb.org/anthology/D14-1181. · DOI · Paper page
  18. Kumar, J., Ye, P., & Doermann, D. (2014). Structural similarity for document image classification and retrieval. Pattern Recognition Letters, 43, 119–126. · DOI
  19. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. ISSN 0006341X, 15410420. URL http://www.jstor.org/stable/2529310.
  20. Le, Q., & Mikolov, T. (2014). Distributed representations of sentences and documents. In Proceedings of the 31st International Conference on International Conference on Machine Learning, (pp. 1188–1196).
  21. Lewis, D., Agam, G., Argamon, S., Frieder, O., Grossman, D., & Heard, J. (2006). Building a test collection for complex document information processing. In Proceedings of the 29th Annual International ACM SIGIR Conference (pp. 665–666). · DOI
  22. Niekler, A., & Jähnichen, P. (2012). Matching results of latent dirichlet allocation for text. In Proceedings of the 11th International Conference on Cognitive Modeling (pp. 317–322). Universitätsverlag der TU Berlin.
  23. Noce, L., Gallo, I., Zamberletti, A., & Calefati, A. (2016). Embedded textual content for document image classification with convolutional neural networks. In Proceedings of the 2016 ACM Symposium on Document Engineering (pp. 165–173). ACM: New York. ISBN 978-1-4503-4438-8. https://doi.org/10.1145/2960811.2960814. · DOI
  24. Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. https://doi.org/10.1109/tsmc.1979.4310076. · DOI
  25. Phan, X. H., Nguyen, C. T., Le, D. T., Nguyen, L. M., & Horiguchi, S. (2011). A hidden topic-based framework toward building applications with short web documents. IEEE Transactions on Knowledge and Data Engineering, 23(7), 961–976. https://doi.org/10.1109/TKDE.2010.27. · DOI
  26. Rusiñol, M., Frinken, V., Karatzas, D., Bagdanov, A. D., & Lladós, J. (2014). Multimodal page classification in administrative document image streams. International Journal on Document Analysis and Recognition, 17(4), 331–341. https://doi.org/10.1007/s10032-014-0225-8. · DOI · Paper page
  27. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. CoRR, arXiv:1409.1556, URL http://arxiv.org/abs/1409.1556. · arXiv
  28. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Aidan N., K., Ł ukasz, & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems 30 (pp. 5998–6008). Curran Associates, URL http://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf.
  29. Wiedemann, G. (2019). Proportional classification revisited: Automatic content analysis of political manifestos using active learning. Social Science Computer Review, 37(2), 135–159. https://doi.org/10.1177/0894439318758389. · DOI
  30. Wiedemann, G., Ruppert, E., Jindal, R., & Biemann, C. (2018). Transfer learning from LDA to BiLSTM-CNN for offensive language detection in Twitter. In Proceedings of GermEval Task 2018, 14th Conference on Natural Language Processing (Konvens) (pp. 85–94). Vienna, Austria: Austrian Academy of Sciences.

Metadata

Authors
Wiedemann, G., & Heyer, G.
Venue
Language Resources and Evaluation
Publisher
Springer Science and Business Media LLC
Published
2019-09-27
Volume
55
Issue
1
Pages
127-150
ISSN
  • 1574-020X
  • 1574-0218
DOI
10.1007/s10579-019-09476-2
Cited by
19
Language
en
Licence

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: Multi-modal page stream segmentation with convolutional neural networks. https://app.cochat.ai/papers/10.1007/s10579-019-09476-2
The paper
Wiedemann, G., & Heyer, G. (2021). Multi-modal page stream segmentation with convolutional neural networks. Language Resources and Evaluation. https://doi.org/10.1007/s10579-019-09476-2