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Proceedings Paper

Unsupervised categorization method of graphemes on handwritten manuscripts: application to style recognition
Author(s): H. Daher; D. Gaceb; V. Eglin; S. Bres; N. Vincent
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Paper Abstract

We present in this paper a feature selection and weighting method for medieval handwriting images that relies on codebooks of shapes of small strokes of characters (graphemes that are issued from the decomposition of manuscripts). These codebooks are important to simplify the automation of the analysis, the manuscripts transcription and the recognition of styles or writers. Our approach provides a precise features weighting by genetic algorithms and a highperformance methodology for the categorization of the shapes of graphemes by using graph coloring into codebooks which are applied in turn on CBIR (Content Based Image Retrieval) in a mixed handwriting database containing different pages from different writers, periods of the history and quality. We show how the coupling of these two mechanisms 'features weighting - graphemes classification' can offer a better separation of the forms to be categorized by exploiting their grapho-morphological, their density and their significant orientations particularities.

Paper Details

Date Published: 24 January 2012
PDF: 9 pages
Proc. SPIE 8297, Document Recognition and Retrieval XIX, 82970W (24 January 2012); doi: 10.1117/12.910608
Show Author Affiliations
H. Daher, Institut National des Sciences Appliquées, CNRS, Univ. de Lyon (France)
D. Gaceb, Institut National des Sciences Appliquées, CNRS, Univ. de Lyon (France)
V. Eglin, Institut National des Sciences Appliquées, CNRS, Univ. de Lyon (France)
S. Bres, Institut National des Sciences Appliquées, CNRS, Univ. de Lyon (France)
N. Vincent, Lab. d'Informatique Paris Descartes, CNRS, Univ. Paris Descartes (France)


Published in SPIE Proceedings Vol. 8297:
Document Recognition and Retrieval XIX
Christian Viard-Gaudin; Richard Zanibbi, Editor(s)

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