HANDWRITTEN TEXT RECOGNITION FOR CROATIAN GLAGOLITIC.
The paper presents and discusses recent advances in Handwritten Text Recognition (HTR) technologies for handwritten and early printed texts in Croatian Glagolitic script. After elaborating on the general principles of training HTR models with respect to the Transkribus platform used for these experi...
| Publicado en: | Slovo (05836255) no. 72; pp. 181 - 193 |
|---|---|
| Autor principal: | |
| Formato: | Artículo |
| Publicado: |
Slovo
2022
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=159920867&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 159920867 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 05836255 2EMB jtl: Slovo (05836255) issn: 05836255 maglogo: N pubinfo: dt: 2022 iid: 72 pid: 30640 pub: Slovo artinfo: ui: 159920867 10.31745/s.72.5 ppf: 181 ppct: 12 formats: fmt: @attributes: type: P size: 2.9MB tig: atl: HANDWRITTEN TEXT RECOGNITION FOR CROATIAN GLAGOLITIC. aug: au: RABUS, Achim affil: University of Freiburg. su: Text recognition Croats Error rates Manuscript preparation (Authorship) Keywords sug: subj: Text recognition Croats Error rates Manuscript preparation (Authorship) Keywords keyword: Digital Humanities early printings Glagolitic script HandwrittenTextRecognition manuscripts digitalna humanistika glagoljica rane tiskane knjige rukopisi strojno prepoznavanje rukopisnoga teksta ab: The paper presents and discusses recent advances in Handwritten Text Recognition (HTR) technologies for handwritten and early printed texts in Croatian Glagolitic script. After elaborating on the general principles of training HTR models with respect to the Transkribus platform used for these experiments, the characteristics of the models trained are discussed. Specifically, the models use the Latin script to transcribe the Glagolitic source. In doing so, they transcribe ligatures and resolve abbreviations correctly in the majority of cases. The computed error rate of the models is below 6%, real-world performance seems to be similar. Using the models for pre-transcription can save a great amount of time when editing manuscripts and, thanks to fuzzy search (keyword spotting), even uncorrected HTR transcriptions can be used for various kinds of analysis. The models are publicly available via the Transkribus platform. Every scholar working on Glagolitic manuscripts and early printings is encouraged to use them. U radu se predstavljaju nedavni pomaci u tehnologiji prepoznavanja rukopisnoga teksta (HTR) namijenjenoj hrvatskoglagoljskim rukopisnim i ranim tiskanim knjigama. Nakon opisivanja općih načela strojne obuke HTR modela, iznose se značajke načela strojnoga učenja u platformi Transkribus, pogotovo modeli korištenja latinice u preslovljavanju glagoljskih tekstova. Pri tome se u većini slučajeva ispravno preslovljavaju ligature i razrješuju kratice. Dobivena čestota pogrešaka je manja od 6%, poput uobičajene čestote pogrešaka kada preslovljavanje provode stručne osobe. Primjena HTR modela u prvom stadiju preslovljavanja može uštedjeti puno vremena pri pripemi i uređivanju rukopisa za objavu, zahvaljujući pretraživanju (pretrazi po ključnim riječima), pa čak i neispravno HTR preslovljavanje može biti korišteno za različite raščlambe. Modeli su javno dostupni posredstvom platforme Transkribus. Potičemo sve znanstvenike koji obrađuju glagoljske rukopise i rane tiskane knjige da se njima koriste. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Slovo (05836255) is the property of Slovo and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Slovo (05836255) holder: Slovo dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
|---|