thumbnail

An Automatic Word-spotting Framework for Medieval Manuscripts

Ruggero Pintus, Ying Yang, Holly Rushmeier, and Enrico Gobbetti

September 2015

Abstract

We present a completely automatic and scalable framework to perform query-by-example word-spotting on medieval manuscripts. Our system does not require any human intervention to produce a large amount of annotated training data, and it provides Computer Vision researchers and Cultural Heritage practitioners with a compact and efficient system for document analysis. We have executed the pipeline both in a single-manuscript and a cross-manuscript setup, and we have tested it on a heterogeneous set of medieval manuscripts, that includes a variety of writing styles, languages, image resolutions, levels of conservation, noise and amount of illumination and ornamentation. We also present a precision/recall based analysis to quantitatively assess the quality of the proposed algorithm.

Reference and download information

Ruggero Pintus, Ying Yang, Holly Rushmeier, and Enrico Gobbetti. An Automatic Word-spotting Framework for Medieval Manuscripts. In Proc. Digital Heritage. Pages 5-12, September 2015.

Related multimedia productions

Bibtex citation record

@InProceedings{Pintus:2015:AWF,
    author = {Ruggero Pintus and Ying Yang and Holly Rushmeier and Enrico Gobbetti},
    title = {An Automatic Word-spotting Framework for Medieval Manuscripts},
    booktitle = {Proc. Digital Heritage},
    pages = {5--12},
    month = {September},
    year = {2015},
    isbn = {978-1-5090-0254-2},
    abstract = { We present a completely automatic and scalable framework to perform query-by-example word-spotting on medieval manuscripts. Our system does not require any human intervention to produce a large amount of annotated training data, and it provides Computer Vision researchers and Cultural Heritage practitioners with a compact and efficient system for document analysis. We have executed the pipeline both in a single-manuscript and a cross-manuscript setup, and we have tested it on a heterogeneous set of medieval manuscripts, that includes a variety of writing styles, languages, image resolutions, levels of conservation, noise and amount of illumination and ornamentation. We also present a precision/recall based analysis to quantitatively assess the quality of the proposed algorithm. },
    url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Pintus:2015:AWF'},
}