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2020, br. 37, str. 35-46
Pretražive digitalne rukopisne kolekcije - mogućnost za raščitavanje srpske ćirilice
Univerzitetska biblioteka "Svetozar Marković", Beograd

e-adresaandonovski@unilib.rs, dakic@unilib.rs, aleksandra@unilib.rs
Ključne reči: biblioteke; arhivska građa; rukopisna građa; projekat READ; Transkribus; transkripcija; neuronske mreže; Handwritten Text Recognition - HTR; Keyword Spotting - KWS
Sažetak
Poslednjih nekoliko godina, biblioteke i arhivi, shvatajući značaj digitalizacije, prvenstveno u pogledu dostupnosti sadržaja, sve više svoju bogatu rukopisnu građu prevode u digitalni format kako bi, sa jedne strane, postala dostupna korisnicima, a sa druge strane, kako bi je sačuvali od propadanja. Koncept virtuelnog istraživačkog okruženja, nastao je kao deo Projekta za prepoznavanje i obogaćivanje arhivskih dokumenata (Recognition and Enrichment of Archival Documents - H2020 READ) i ima potencijal da omogući potpuno novi pristup istorijskim rukopisnim dokumentima koji se čuvaju u institucijama kulture širom Evrope. Glavni cilj READ projekta bio je da se izgradi virtuelno istraživačko okruženje u okviru koga bi se razvijale vrhunske tehnologije za automatsko prepoznavanje, transkripciju, indeksiranje i obogaćivanje rukopisnih arhivskih dokumenata. Univerzitetska biblioteka "Svetozar Marković" se na samom početku uključila u ovaj projekat, kao pridruženi partner, sa ciljem da se razvije alat koji će omogućiti raščitavanje srpske ćirilične rukopisne građe.
Reference
*** Recognition and enrichment of archival documents. Preuzeto 12.8.2020, http://observatory.rich2020.eu/rich/projects/view/313331
*** Revolutioniying access to handwritten documents European Cooperative Society. Preuzeto 30.7.2020, https://readcoop.eu
Diem, M., Kleber, F., Fiel, S., Grünin, T., Gatos, B. (2017) cBAD: ICDAR2017 Competition on Baseline Detection. u: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 1: 1355-1360, Preuzeto 2.8.2020, https://api.semanticscholar.org/CorpusID:4761833
Digitisation and Digital Preservation Group (DEA Group) Preuzeto 1.8.2020, https://www.uibk.ac.at/germanistik/einrichtungen/dea.html
Gatos, B., Louloudis, G., Causer, T., Grint, K., Romero, V., Sánchez, J., Toselli, A.H., Vidal, E. (2014) Ground-truth production in the Transcriptorium project. u: 11th IAPR International Workshop on Document Analysis Systems, 237-241, Preuzeto 2.8.2020, https://api.semanticscholar.org/CorpusID:12688730
Giotis, A.P., Sfikas, G., Gatos, B., Nikou, C. (2017) A survey of document image word spotting techniques. Pattern Recognition, 68: 310-332, Preuzeto 2.8.2020, https://www.sciencedirect.com/science/ article/abs/pii/S0031320317300870?via%3Dihub
GitHub Transkribus. Preuzeto 3.8.2020, https://github.com/transkribus
Grüning, T., Leifert, G., Strauss, T., Labahn, R. (2017) A Robust and Binarization-free approach for text line detection in historical documents. u: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 1: 236-241, Preuzeto 3.8.2020, https://ieeexplore.ieee.org/abstract/document/8269978
Johannes, M., Weidemann, M., Labahn, R. Deliverable 7.9, HTR engine based on neural networks P3. Deliverable submitted to the European Commission, Preuzeto 4. 8. 2020, https://read.transkribus.eu/wp-content/uploads/2018/12/Del_D7_9.pdf
Kahle, P., Colutto, S., Hackl, G., Mühlberger, G. (2017) Transkribus: A service platform for transcription, recognition and retrieval of historical documents. u: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 4: 19-24, Preuzeto 3.8.2020, https://api.semanticscholar.org/CorpusID:25099654
Leifert, G., Strauß, T., Grüning, T., Labahn, R. (2014) CITlab ARGUS for historical handwritten documents. ArXivabs/1412.3949, Preuzeto 3.8.2020, http://arxiv.org/abs/1605.08412
Mühlberger, G. (2016) H2020 Project READ (Recognition and enrichment of archival documents) -2016-2019. Preuzeto 2.8.2020, http://www.academia.edu/22653102/ H2020_Project_READ_Recognition_and_Enrichment_of_Archival_Documents_-_2016-2019
Mühlberger, G., et al. (2019) Transforming scholarship in the archives through handwritten text recognition. Journal of Documentation, 75: 954-976, Preuzeto 4.8.2020, https://api.semanticscholar.org/CorpusID:196204627
Romero, V., Bosch, V., Hernández-Tornero, C., Vidal, E., Sánchez, J. (2017) A historical document handwriting transcription end-to-end system. u: Alexandre L., Salvador Sánchez J., Rodrigues J. [ur.] Pattern Recognition and Image Analysis, Springer International Publishing, 10255: 149-157, Lecture Notes in Computer Science
Sánchez, J., Romero, V., Toselli, A.H., Vidal, E. (2018) Handwritten text recognition competitions with the transcriptorium dataset. u: Document Analysis and Text Recognition, World Scientific Publishing, 213-239
Seaward, L., Kallio, M. Transkribus: Handwritten text recognition technology for historical documents. Preuzeto 4.8.2020, https://dh2017.adho.org/abstracts/649/649.pdf
 

O članku

jezik rada: srpski
vrsta rada: pregledni članak
DOI: 10.19090/cit.2020.37.35-46
primljen: 24.08.2020.
revidiran: 07.10.2020.
prihvaćen: 12.10.2020.
objavljen u SCIndeksu: 23.12.2020.

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