LATEST UPDATES

(6/12/2017) Links to additional resources and to competitor’s open source code in Resource section
(6/12/2017) The results of ICDAR2017 CLAMM competition are published and online.
(27/11/2017) The Data Sets for the ICDAR2017 CLAMM competition are now available (Training dataset, Tasks 1 & 3, Tasks 2 & 4).
(1/03/2017) The Training Data Set for the ICDAR2017 CLAMM competition is now available.

Contents

1 Rationale of the CLaMM competition
2 Works Cited

Rationale of the CLaMM competition

The tasks to be evaluated in the competition based on the CLaMM corpus (CLaMM : classification of Latin Medieval Manuscripts) are related to the classification of images of Latin Scripts, from handwritten books dated 500 C.E. to 1600 C.E.

Automated analysis and classification of handwritings applied to the written production of the European Middle Ages is a new challenge and a frontier in handwriting recognition and document analysis and recognition.

Context

Digital libraries from Cultural Heritage institutions contain literally ten-thousands of digitized manuscripts of the European Middle Ages. Some examples:

more than 12,000 fully digitized manuscripts in Manuscripta Mediaevalia (必利勁
uscripta-mediaevalia.de/ »>http://www.manuscripta-mediaevalia.de/);
10,000 in Gallica (http://gallica.bnf.fr), the digital library of the French National Library;
7,700 in the BVMM – Bibliothèque Virtuelle des Manuscrits Médiévaux (http://bvmm.irht.cnrs.fr), or Virtual Library of Medieval Manuscripts, run by the Institut de Recherche et d’Histoire des Textes (CNRS);
4,000 in Teca Digitale (http://teca.bmlonline.it/), digital library of the Biblioteca Medicea Laurenziana;
2,000 « Digitised Manuscripts » on the website of the British Library (http://www.bl.uk/manuscripts/);
1,400 in e-codices – Virtual Manuscript Library of Switzerland (http://www.e-codices.ch/en);
400 in the Digital Walters (http://www.thedigitalwalters.org/), digital library of the Walters Art Museum (Baltimore)
300 in the « Beinecke Digital Collections » (http://brbl-dl.library.yale.edu/) of the Beinecke Rare Book & Manuscript Library (Yale University Library);
as well as 5447 (often partially) digitise犀利士
d manuscripts in Digital Scriptorium (http://vm136.lib.berkeley.edu/BANC/digitalscriptorium/)

The overwhelming majority of manuscripts in there are written in Latin script.

In this context, there is a need for an automated “tagging” or “cataloguing” of the handwriting on the images, not only to allow for historical research (when and how which text is written), but primarily because it is a pre-requisite for handwritten text recognition (HTR) or automated indexing and data mining. To perform HTR on the digitized manuscripts, one “numerical model” is necessary to recognize the text for each script type and the identification of the script type is the first step.

This has been stated for the modern handwriting styles [1]. The medieval millennium extending from 500 C.E. to 1600 C.E. shows that the Latin script evolved and took very different forms, much more diverse than all the writing styles of the 19^th to the 21^st century.

3 Importance of the CLaMM dataset

The participants of this competition will get the only available reference data-set covering the European Middle Age and tagged as regards script types and production date.

In real-life conditions and beyond the challenges of material degradations, segmentation, etc., one of the difficulties is that there is a historical continuum in the evolution of scripts so that there are mixed types and many scripts that could pertain to two or more categories. In this regard, classification of scripts addresses the subjectivity of the human mind, so that, as in art history, all attributions remain subject to debate and discussion.

Works Cited

[1] R. Niels and L. Vuurpijl, “Generating Copybooks from Consistent Handwriting Styles,” in Ninth International Conference on Document Analysis and Recognition, 2007. ICDAR 2007, 2007, vol. 2, pp. 1009–1013.

[2] L. R. B. Schomaker, K. Franke, and M. L. Bulacu, “Using codebooks of fragmented connected-component contours in forensic and historic writer identification,” Pattern Recognition Letters, vol. 28, no. 6, pp. 719–727, 2007.

[3] A. A. Brink, J. Smit, M. L. Bulacu, and L. R. B. Schomaker, “Writer identification using directional ink-trace width measurements,” Pattern Recognition, vol. 45, no. 1, pp. 162–171, 2012.

[4] S. He, M. Wiering, and L. R. B. Schomaker, “Junction detection in handwritten documents and its application to writer identification,” Pattern Recognition, vol. 48, pp. 4036–4048, 2015.

[5] D. Muzerelle and M. Gurrado, Eds., Analyse d’image et paléographie systématique : travaux du programme “Graphem” : communications présentées au colloque international “Paléographie fondamentale, paléographie expérimentale : l’écriture entre histoire et science” (Institut de recherche et d’histoire des textes (CNRS), Paris, 14-15 avril 2011). Paris: Association Gazette du livre médiéval, 2011.

[6] D. Stutzmann and M. Gurrado, “Mesure et histoire des écritures médiévales,” in Mesure et histoire médiévale, Actes du XLIIIe Congrès de la SHMESP, Paris: Publications de la Sorbonne, 2013, pp. 153–166.

[7] N. Vincent, A. Seropian, and G. Stamon, “Synthesis for handwriting analysis,” Pattern Recognition Letters, vol. 26, no. 3, pp. 267–275, 2005.

[8] G. Joutel, V. Eglin, and H. Emptoz, “Generic scale-space process for handwriting documents analysis,” in 19th International Conference on Pattern Recognition, 2008. ICPR 2008, 2008, pp. 1–4.

[9] I. Siddiqi, F. Cloppet, and N. Vincent, “Contour Based Features for the Classification of Ancient Manuscripts,” presented at the 14th Conference of the International Graphonolics Society, (IGS), Dijon, 2009.

[10] G. Joutel, V. Eglin, and H. Emptoz, “Generic Scale-Space Architecture for Handwriting Documents Analysis, chapter 15,” in Pattern Recognition Recent Advances, A. Herout, Ed. InTech, 2010, pp. 293–312.

[11] F. Cloppet, H. Daher, V. Églin, H. Emptoz, M. Exbrayat, G. Joutel, F. Lebourgeois, L. Martin, I. Moalla, I. Siddiqi, and N. Vincent, “New Tools for Exploring, Analysing and Categorising Medieval Scripts,” Digital Medievalist, no. 7, 2011.

[12] H. Daher, V. Églin, S. Brès, and N. Vincent, “Étude de la dynamique des écritures médiévales. Analyse et classification des formes écrites,” Gazette du livre médiéval, vol. 56–57, pp. 21–41, 2011.

[13] I. Siddiqi, F. Cloppet, and N. Vincent, “Writing property descriptors. A proposal for typological groupings,” Gazette du livre médiéval, vol. 56–57, pp. 42–57, 2011.

[14] V. Eglin, D. Gaceb, H. Daher, S. Bres, and N. Vincent, “Outils d’analyse de la dynamique des écritures médiévales pour l’aide à l’expertise paléographique,” Document Numérique, vol. 41, no. 1, pp. 81–104, 2011.

[15] D. Stutzmann, “Clustering of medieval scripts through computer image analysis: towards an evaluation protocol,” Digital Medievalist, vol. 10, 2015

Classification of Medieval Handwritings in Latin Script