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.
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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. }, isbn = {978-1-5090-0254-2}, url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Pintus:2015:AWF'}, }
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