nach oben

International Journal on Document Analysis and Recognition (IJDAR)

Erschienen in:

30.01.2023 | Special Issue Paper

Large-scale genealogical information extraction from handwritten Quebec parish records

verfasst von: Solène Tarride, Martin Maarand, Mélodie Boillet, James McGrath, Eugénie Capel, Hélène Vézina, Christopher Kermorvant

Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) | Ausgabe 3/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This paper presents a complete workflow designed for extracting information from Quebec handwritten parish registers. The acts in these documents contain individual and family information highly valuable for genetic, demographic and social studies of the Quebec population. From an image of parish records, our workflow is able to identify the acts and extract personal information. The workflow is divided into successive steps: page classification, text line detection, handwritten text recognition, named entity recognition and act detection and classification. For all these steps, different machine learning models are compared. Once the information is extracted, validation rules designed by experts are then applied to standardize the extracted information and ensure its consistency with the type of act (birth, marriage and death). This validation step is able to reject records that are considered invalid or merged. The full workflow has been used to process over two million pages of Quebec parish registers from the 19–20th centuries. On a sample comprising 65% of registers, 3.2 million acts were recognized. Verification of the birth and death acts from this sample shows that 74% of them are considered complete and valid. These records will be integrated into the BALSAC database and linked together to recreate family and genealogical relations at large scale.

Vorheriger Artikel Historical document image analysis using controlled data for pre-training

Nächster Artikel Printed Ottoman text recognition using synthetic data and data augmentation

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Abadie, N., Carlinet, E., Chazalon, J., Duménieu, B.: A Benchmark of Named Entity Recognition Approaches in Historical Documents Application to 19th Century French Directories. In: Document Analysis Systems. pp. 445–460 (2022)

Akbik, A., Blythe, D., Vollgraf, R.: Contextual String Embeddings for Sequence Labeling. In: Proceedings of the 27th International Conference on Computational Linguistics. pp. 1638–1649 (Aug 2018)

Ares Oliveira, S., Seguin, B., Kaplan, F.: dhSegment: A Generic Deep-learning Approach for Document Segmentation. In: 16th International Conference on Frontiers in Handwriting Recognition (ICFHR). pp. 7–12 (Aug 2018)

Arora, A., Chang, C.C., Rekabdar, B., BabaAli, B., Povey, D., Etter, D., Raj, D., Hadian, H., Trmal, J., Garcia, P., et al.: Using ASR Methods for OCR. In: 15th International Conference on Document Analysis and Recognition. pp. 663–668 (Sep 2019)

Bluche, T., Louradour, J., Messina, R.O.: Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention. In: International Conference on Document Analysis and Recognition. pp. 1050–1055 (Nov 2017). https://doi.org/10.1109/ICDAR.2017.174

Boillet, M., Maarand, M., Paquet, T., Kermorvant, C.: Including Keyword Position in Image-Based Models for Act Segmentation of Historical Registers. In: 6th International Workshop on Historical Document Imaging and Processing. p. 31-36 (Sep 2021). https://doi.org/10.1145/3476887.3476905

Boillet, M., Kermorvant, C., Paquet, T.: Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks. In: 25th International Conference on Pattern Recognition. pp. 2134–2141 (Jan 2020)

Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: Identifying Density-based Local Outliers. In: 2000 ACM SIGMOD International Conference on Management of Data. pp. 93–104 (2000)

Capobianco, S., Marinai, S.: Deep Neural Networks for Record Counting in Historical Handwritten Documents. Pattern Recogn. Lett. 119, 103–111 (2017). https://doi.org/10.1016/j.patrec.2017.10.023CrossRef

10.

Carbonell, M., Fornés, A., Villegas, M., Lladós, J.: A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages. Pattern Recogn. Lett. 136, 219–227 (2020). https://doi.org/10.1016/j.patrec.2020.05.001CrossRef

11.

Carbonell, M., Villegas, M., Fornés, A., Lladós, J.: Joint recognition of handwritten text and named entities with a neural end-to-end model. In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). pp. 399–404. IEEE Computer Society, Los Alamitos, CA, USA (apr 2018). 10.1109/DAS.2018.52, https://doi.ieeecomputersociety.org/10.1109/DAS.2018.52

12.

Constum, T., Kempf, N., Paquet, T., Tranouez, P., Chatelain, C., Brée, S., Merveille, F.: Recognition and Information Extraction in Historical Handwritten Tables: Toward Understanding Early 20th Century Paris Census. In: Document Analysis Systems. pp. 143–157 (2022)

13.

Coquenet, D., Chatelain, C., Paquet, T.: DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition (2022). 10.48550/ARXIV.2203.12273

14.

Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: ImageNet: a Large-Scale Hierarchical Image Database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. pp. 248–255 (Jun 2009). https://doi.org/10.1109/CVPR.2009.5206848

15.

Douzon, T., Duffner, S., Garcia, C., Espinas, J.: Improving Information Extraction on Business Documents with Specific Pre-training Tasks. In: Document Analysis Systems. pp. 111–125 (2022)

16.

Embley, D.W., Nagy, G.: Green Interaction for Extracting Family Information from OCR’d Books. In: 2018 13th IAPR International Workshop on Document Analysis Systems. pp. 127–132 (2018). https://doi.org/10.1109/DAS.2018.58

17.

Fornés, A., Romero, V., Baró, A., Toledo, J.I., Sánchez, J.A., Vidal, E., Lladós, J.: ICDAR2017 Competition on Information Extraction in Historical Handwritten Records. In: 2017 14th IAPR International Conference on Document Analysis and Recognition. vol. 01, pp. 1389–1394 (2017). https://doi.org/10.1109/ICDAR.2017.227

18.

Grüning, T., Labahn, R., Diem, M., Kleber, F., Fiel, S.: READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents. In: 13th International Workshop on Document Analysis Systems. pp. 351–356 (May 2017)

19.

Grüning, T., Leifert, G., Strauß, T., Labahn, R.: A Two-Stage Method for Text Line Detection in Historical Documents. In: International Journal on Document Analysis and Recognition. vol. 22, pp. 285–302 (Sep 2019). https://doi.org/10.1007/s10032-019-00332-1

20.

Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On Calibration of Modern Neural Networks. In: International Conference on Machine Learning (2017)

21.

He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778 (Jun 2016). https://doi.org/10.1109/CVPR.2016.90

22.

Honnibal, M., Montani, I., Van Landeghem, S., Boyd, A.: spaCy: Industrial-strength Natural Language Processing in Python (2020). https://doi.org/10.5281/zenodo.1212303,

23.

Kahle, P., Colutto, S., Hackl, G., Mühlberger, G.: Transkribus - A Service Platform for Transcription, Recognition and Retrieval of Historical Documents. In: 2017 14th IAPR International Conference on Document Analysis and Recognition. vol. 04, pp. 19–24 (Nov 2017). https://doi.org/10.1109/ICDAR.2017.307

24.

Kiss, M., Kohút, J., Benes, K., Hradis, M.: Importance of Textlines in Historical Document Classification. In: Document Analysis Systems. pp. 158–170 (2022)

25.

Lang, E., Puigcerver, J., Toselli, A.H., Vidal, E.: Probabilistic Indexing and Search for Information Extraction on Handwritten German Parish Records. In: 2018 16th International Conference on Frontiers in Handwriting Recognition. pp. 44–49 (2018). https://doi.org/10.1109/ICFHR-2018.2018.00017

26.

Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation Forest. In: 2008 Eighth IEEE International Conference on Data Mining. pp. 413–422 (2008)

27.

Liu, X., Gao, F., Zhang, Q., Zhao, H.: Graph Convolution for Multimodal Information Extraction from Visually Rich Documents. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). pp. 32–39 (Jun 2019). https://doi.org/10.18653/v1/N19-2005

28.

Maarand, M., Beyer, Y., Kåsen, A., Fosseide, K.T., Kermorvant, C.: A comprehensive comparison of open-source libraries for handwritten text recognition in norwegian. In: Document Analysis Systems. pp. 399–413 (2022)

29.

Martin, L., Muller, B., Ortiz Suárez, P.J., Dupont, Y., Romary, L., de la Clergerie, É., Seddah, D., Sagot, B.: CamemBERT: a Tasty French Language Model. In: 58th Annual Meeting of the Association for Computational Linguistics. pp. 7203–7219 (2020)

30.

Monnier, T., Aubry, M.: docExtractor: An off-the-shelf historical document element extraction. In: International Conference on Frontiers in Handwriting Recognition (2020)

31.

Bizon Monroc, C., Miret, B., Bonhomme, M.L., Kermorvant, C.: A Comprehensive Study of Open-Source Libraries for Named Entity Recognition on Handwritten Historical Documents. In: Document Analysis Systems. pp. 429–444 (2022)

32.

Nion, T., Menasri, F., Louradour, J., Sibade, C., Retornaz, T., Métaireau, P.Y., Kermorvant, C.: Handwritten Information Extraction from Historical Census Documents. In: 2013 12th International Conference on Document Analysis and Recognition. pp. 822–826 (2013). https://doi.org/10.1109/ICDAR.2013.168

33.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH

34.

Prieto, J.R., Bosch, V., Vidal, E., Stutzmann, D., Hamel, S.: Text Content Based Layout Analysis. In: 2020 17th International Conference on Frontiers in Handwriting Recognition. pp. 258–263 (Sep 2020). https://doi.org/10.1109/ICFHR2020.2020.00055

35.

Puigcerver, J.: Are Multidimensional Recurrent Layers Really Necessary for Handwritten Text Recognition? In: 2017 14th IAPR International Conference on Document Analysis and Recognition. vol. 01, pp. 67–72 (2017). https://doi.org/10.1109/ICDAR.2017.20

36.

Qi, P., Zhang, Y., Zhang, Y., Bolton, J., Manning, C.D.: Stanza: A Python Natural Language Processing Toolkit for Many Human Languages. In: 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. pp. 101–108 (Jul 2020). https://doi.org/10.18653/v1/2020.acl-demos.14

37.

Rouhou, A.C., Dhiaf, M., Kessentini, Y., Salem, S.B.: Transformer-based Approach for Joint Handwriting and Named Entity Recognition in Historical Document. Pattern Recogn. Lett. 155, 128–134 (2022). https://doi.org/10.1016/j.patrec.2021.11.010CrossRef

38.

Sennrich, R., Haddow, B., Birch, A.: Neural Machine Translation of Rare Words with Subword Units. In: Annual Meeting of the Association for Computational Linguistics (2016)

39.

Seuret, M., Nicolaou, A., Rodríguez-Salas, D., Weichselbaumer, N., Stutzmann, D., Mayr, M., Maier, A., Christlein, V.: ICDAR 2021 Competition on Historical Document Classification. In: Lladós, J., Lopresti, D., Uchida, S. (eds.) International Conference on Document Analysis and Recognition. pp. 618–634 (2021)

40.

Simistira, F., Seuret, M., Eichenberger, N., Garz, A., Liwicki, M., Ingold, R.: DIVA-HisDB: A Precisely Annotated Large Dataset of Challenging Medieval Manuscripts. In: 15th International Conference on Frontiers in Handwriting Recognition. pp. 471–476 (Oct 2016). https://doi.org/10.1109/ICFHR.2016.0093

41.

Tarride, S., Lemaitre, A., Coüasnon, B., Tardivel, S.: Combination of deep neural networks and logical rules for record segmentation in historical handwritten registers using few examples. Int. J. Doc. Anal. Recogn. 24, 77–96 (2021). https://doi.org/10.1007/s10032-021-00362-8

42.

Tarride, S., Lemaitre, A., Coüasnon, B., Tardivel, S.: A Comparative Study of Information Extraction Strategies Using an Attention-Based Neural Network. In: Document Analysis Systems. pp. 644–658 (2022)

43.

Walton, S., Livermore, L., Bánki, O., N. Cubey, R.W., Drinkwater, R., Englund, M., Goble, C., Groom, Q., Kermorvant, C., Rey, I., M Santos, C., Scott, B., R. Williams, A., Wu, Z.: Landscape analysis for the specimen data refinery. Research Ideas and Outcomes 6, e57602 (2020). https://doi.org/10.3897/rio.6.e57602

44.

Wang, J., Liu, C., Jin, L., Tang, G., Zhang, J., Zhang, S., Wang, Q., Wu, Y., Cai, M.: Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution. In: Proceedings of the AAAI Conference on Artificial Intelligence (2021)

45.

Wigington, C., Tensmeyer, C., Davis, B., Barrett, W., Price, B., Cohen, S.: Start, Follow, Read: End-to-End Full-Page Handwriting Recognition. In: ECCV 2018: 15th European Conference. p. 372-388 (2018). https://doi.org/10.1007/978-3-030-01231-1_23

46.

Xu, Y., Li, M., Cui, L., Huang, S., Wei, F., Zhou, M.: LayoutLM: Pre-training of Text and Layout for Document Image Understanding. In: 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. p. 1192-1200 (Aug 2020)

47.

Yu, W., Lu, N., Qi, X., Gong, P., Xiao, R.: PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks. 2020 25th International Conference on Pattern Recognition pp. 4363–4370 (2021)

Titel: Large-scale genealogical information extraction from handwritten Quebec parish records
verfasst von: Solène Tarride
Martin Maarand
Mélodie Boillet
James McGrath
Eugénie Capel
Hélène Vézina
Christopher Kermorvant
Publikationsdatum: 30.01.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) / Ausgabe 3/2023
Print ISSN: 1433-2833
Elektronische ISSN: 1433-2825
DOI: https://doi.org/10.1007/s10032-023-00427-w

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 3/2023

Line extraction in handwritten documents via instance segmentation

Editorial for special issue on “advanced topics in document analysis and recognition”

Scheme for palimpsests reconstruction using synthesized dataset

Historical document image analysis using controlled data for pre-training

Inv3D: a high-resolution 3D invoice dataset for template-guided single-image document unwarping

End-to-end optical music recognition for pianoform sheet music

Premium Partner