Aghbari, Z., Brook, S.: HAH manuscripts: a holistic paradigm for classifying and retrieving historical Arabic handwritten documents. Expert Syst. Appl. 36(8), 10942–10951 (2009)
CrossRef
Google Scholar
Ahmed, R., Al-Khatib, W., Mahmoud, S.: A survey on handwritten documents word spotting. Int. J. Multimed. Inf. Retr. 6(1), 31–47 (2017). https://doi.org/10.1007/s13735-016-0110-y
CrossRef
Google Scholar
Cao, T., Ngo, V.: Semantic search by latent ontological features. Int. J. New Gener. Comput. 30(1), 53–71 (2012). https://doi.org/10.1007/s00354-012-0104-0
CrossRef
Google Scholar
Cheikhrouhou, A., Kessentini, Y., Kanoun, S.: Multi-task learning for simultaneous script identification and keyword spotting in document images. Pattern Recogn. 113, 107832 (2021)
CrossRef
Google Scholar
Colutto, S., Kahle, P., Guenter, H., Muehlberger, G.: Transkribus. A platform for automated text recognition and searching of historical documents. In: Proceedings of the 15th International Conference on eScience (eScience), pp. 463–466 (2019)
Google Scholar
Debruyne, C., et al.: Ireland?s authoritative geospatial linked data. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10588, pp. 66–74. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68204-4_6
CrossRef
Google Scholar
Doerr, M.: The CIDOC conceptual reference module: an ontological approach to semantic interoperability of metadata. AI Mag. 24(3), 75–92 (2003)
Google Scholar
Frinken, V., Palakodety, S.: Handwritten keyword spotting in historical documents. In: Handwritten Historical Document Analysis, Recognition, and Retrieval—State of the Art and Future Trends, Series in MP&AI, vol. 89, pp. 81–99. World Scientific Publishing (2021)
Google Scholar
Gheorghe, R., Hinman, M., Russo, R.: Elasticsearch in Action, 1st edn. Manning Publications Co., Shelter Island (2015)
Google Scholar
Hellmann, S., Lehmann, J., Auer, S., Brümmer, M.: Integrating NLP using linked data. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8219, pp. 98–113. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41338-4_7
CrossRef
Google Scholar
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
Jiang, Y.: Semantically-enhanced information retrieval using multiple knowledge sources. Clust. Comput. 23(4), 2925–2944 (2020). https://doi.org/10.1007/s10586-020-03057-7
CrossRef
Google Scholar
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 (ICDAR), vol. 04, pp. 19–24 (2017). https://doi.org/10.1109/ICDAR.2017.307
Kang, L., Riba, P., Villegas, M., Fornés, A., Rusiñol, M.: Candidate fusion: integrating language modelling into a sequence-to-sequence handwritten word recognition architecture. Pattern Recogn. 112, 107790 (2021)
CrossRef
Google Scholar
Lang, E., Puigcerver, J., Toselli, A.H., Vidal, E.: Probabilistic indexing and search for information extraction on handwritten German parish records. In: Proceedings of 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 44–49 (2018)
Google Scholar
Leydier, Y., Lebourgeois, F., Emptoz, H.: Text search for medieval manuscript images. Pattern Recogn. 40(12), 3552–3567 (2007)
CrossRef
Google Scholar
Li, Z., Wu, Q., Xiao, Y., Jin, M., Lu, H.: Deep matching network for handwritten Chinese character recognition. Pattern Recogn. 107, 107471 (2020)
CrossRef
Google Scholar
Martínek, J., Lenc, L., Král, P.: Building an efficient OCR system for historical documents with little training data. Neural Comput. Appl. 32(23), 17209–17227 (2020). https://doi.org/10.1007/s00521-020-04910-x
CrossRef
Google Scholar
Ngo, V., Cao, T.: Discovering latent concepts and exploiting ontological features for semantic text search. In: Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP-2011), pp. 571–579. ACL (2011)
Google Scholar
Nozza, D., Manchanda, P., Fersini, E., Palmonari, M., Messina, E.: LearningToAdapt with word embeddings: domain adaptation of named entity recognition systems. Inf. Process. Manag. 58(3), 102537 (2021)
CrossRef
Google Scholar
Stauffer, M., Fischer, A., Riesen, K.: Filters for graph-based keyword spotting in historical handwritten documents. Pattern Recogn. Lett. 134, 125–134 (2020)
CrossRef
Google Scholar
Toledo, J., Carbonell, M., Fornés, A., Lladós, J.: Information extraction from historical handwritten document images with a context-aware neural model. Pattern Recogn. 86, 27–36 (2019)
CrossRef
Google Scholar
Vidal, E., et al.: The carabela project and manuscript collection: large-scale probabilistic indexing and content-based classification. In: The 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 85–90 (2020)
Google Scholar
Wang, J., et al.: A pseudo-relevance feedback framework combining relevance matching and semantic matching for information retrieval. Inf. Process. Manag. 57(6), 102342 (2020)
CrossRef
Google Scholar