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ATHENA: Automatic Text Height ExtractioN for the Analysis of text lines in old handwritten manuscripts

Ruggero Pintus, Ying Yang, and Holly Rushmeier

2015

Abstract

Massive digital acquisition and preservation of deteriorating historical and artistic documents is of particular importance due to their value and fragile condition. The study and browsing of such digital libraries is invaluable for scholars in the Cultural Heritage field, but requires automatic tools for analyzing and indexing these datasets. We present two completely automatic methods requiring no human intervention: text height estimation and text line extraction. Our proposed methods have been evaluated on a huge heterogeneous corpus of illuminated medieval manuscripts of different writing styles and with various problematic attributes, such as holes, spots, ink bleed-through, ornamentation, background noise, and overlapping text lines. Our experimental results demonstrate that these two new methods are efficient and reliable, even when applied to very noisy and damaged old handwritten manuscripts.

Reference and download information

Ruggero Pintus, Ying Yang, and Holly Rushmeier. ATHENA: Automatic Text Height ExtractioN for the Analysis of text lines in old handwritten manuscripts. ACM Journal on Computing and Cultural Heritage, 8(1): 1:1-1:25, 2015. https://doi.org/10.1145/2659020.

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Bibtex citation record

@Article{Pintus:2014:ATH,
    author = {Ruggero Pintus and Ying Yang and Holly Rushmeier},
    title = {{ATHENA}: Automatic Text Height ExtractioN for the Analysis of text lines in old handwritten manuscripts},
    journal = {ACM Journal on Computing and Cultural Heritage},
    volume = {8},
    number = {1},
    pages = {1:1--1:25},
    year = {2015},
    abstract = { Massive digital acquisition and preservation of deteriorating historical and artistic documents is of particular importance due to their value and fragile condition. The study and browsing of such digital libraries is invaluable for scholars in the Cultural Heritage field, but requires automatic tools for analyzing and indexing these datasets. We present two completely automatic methods requiring no human intervention: text height estimation and text line extraction. Our proposed methods have been evaluated on a huge heterogeneous corpus of illuminated medieval manuscripts of different writing styles and with various problematic attributes, such as holes, spots, ink bleed-through, ornamentation, background noise, and overlapping text lines. Our experimental results demonstrate that these two new methods are efficient and reliable, even when applied to very noisy and damaged old handwritten manuscripts. },
    url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Pintus:2014:ATH'},
}