Comparing OCR Pipelines for Folkloristic Text Digitization

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dc.contributor.authorMachidon, Octavian M.en_US
dc.contributor.authorMachidon, Alina L.en_US
dc.contributor.editorCampana, Stefanoen_US
dc.contributor.editorFerdani, Danieleen_US
dc.contributor.editorGraf, Holgeren_US
dc.contributor.editorGuidi, Gabrieleen_US
dc.contributor.editorHegarty, Zackaryen_US
dc.contributor.editorPescarin, Sofiaen_US
dc.contributor.editorRemondino, Fabioen_US
dc.date.accessioned2025-09-05T20:05:55Z
dc.date.available2025-09-05T20:05:55Z
dc.date.issued2025
dc.description.abstractThe digitization of historical folkloristic materials presents unique challenges due to diverse text layouts, varying print and handwriting styles, and linguistic variations. This study explores different optical character recognition (OCR) approaches for Slovene folkloristic and historical text digitization, integrating both traditional methods and large language models (LLMs) to improve text transcription accuracy while maintaining linguistic and structural integrity. We compare single-stage OCR techniques with multi-stage pipelines that incorporate machine learning-driven post-processing for text normalization and layout reconstruction. While LLM-enhanced methods show promise in refining recognition outputs and improving readability, they also introduce challenges related to unintended modifications, particularly in the preservation of dialectal expressions and historical structures. Our findings provide insights into selecting optimal digitization strategies for large-scale folklore archives and outline recommendations for developing robust OCR pipelines that balance automation with the need for textual authenticity in digital humanities research.en_US
dc.description.sectionheadersDigitization and Segmentation
dc.description.seriesinformationDigital Heritage
dc.identifier.doi10.2312/dh.20253075
dc.identifier.isbn978-3-03868-277-6
dc.identifier.pages8 pages
dc.identifier.urihttps://doi.org/10.2312/dh.20253075
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/dh20253075
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Information systems → Information systems applications; Applied computing → Arts and humanities
dc.subjectInformation systems → Information systems applications
dc.subjectApplied computing → Arts and humanities
dc.titleComparing OCR Pipelines for Folkloristic Text Digitizationen_US
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