Advancing Armenian Inscription Recognition

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dc.contributor.authorNersesian, Gevorgen_US
dc.contributor.authorSarvazyan, Narineen_US
dc.contributor.authorKhachatryan, Surenen_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.accessioned2025-09-05T20:26:36Z
dc.date.available2025-09-05T20:26:36Z
dc.date.issued2025
dc.description.abstractArmenian monuments are rich in carved stone inscriptions. These inscriptions serve as vital records of cultural and linguistic heritage, offering insights into the lives, beliefs, and traditions of Armenians during the Middle ages. However, detecting and comprehending these inscriptions pose significant challenges. Due to weathering, vandalism, erosion, and the complexity of ancient scripts, many of these texts remain unreadable. Yet, the few existing studies indicate that deciphering these messages from the past is feasible with technological advancements. In the present project we study a unique, newly created and unex- plored collection of digital twins of Armenian tapanakars (tombstones) and khachkars (cross-stones) focusing on hierarchical segmentation of the images using the detected geometrical and statistical features. The results are applied to character classi- fication and the accuracy of the generated images is estimated. Since the detection stage of the algorithm is universal for any kind of shapes, it opens up new research avenues that extend beyond text recognition alone. The same pipeline can be adapted to identify decorative motifs, geometric symbols, and other visual patterns commonly found on tapanakar surfaces.en_US
dc.description.sectionheadersExtracting Knowledge from Digitized Assets
dc.description.seriesinformationDigital Heritage
dc.identifier.doi10.2312/dh.20253359
dc.identifier.isbn978-3-03868-277-6
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/dh.20253359
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/dh20253359
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts Computing methodologies → Computer vision problems; Supervised learning by classification; Information systems → Optical character recognition; Document representation
dc.subjectCCS Concepts Computing methodologies → Computer vision problems
dc.subjectSupervised learning by classification
dc.subjectInformation systems → Optical character recognition
dc.subjectDocument representation
dc.titleAdvancing Armenian Inscription Recognitionen_US
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Track 05 – Analysis and Interpretation