For the evalution of this year’s competition, we have established the training and test data sets, with published the full ground-truth for the validation and test sets. The complete dataset (validation + test) is publicly available at the following address: 必利勁 281/zenodo.3262372″>https://doi.org/10.5281/zenodo.3262372

The training set is described in S. Fiel, F. Kleber, M. Diem, V. Christlein, G. Louloudis, S. Nikos, and B. Gatos, “Icdar2017 competition on historical document writer identification (historical-wi),” in 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 01, Nov 2017, pp. 1377–1382.

The validation set encompasses 1200 images and the test set 20000 images.

The provenance and distribution of the test set is as described in the following chart.

The test data set contains 20,000 images with different, i.e. 1-5 samples per writer . The corpus can be downloaded at the following link: 犀利士 href= »https://faubox.rrze.uni-erlangen.de/dl/fiCb7MhhW8nyM9Vy8d9KUxZu/wi_comp_19_test.zip »>https://faubox.rrze.uni-erlangen.de/dl/fiCb7MhhW8nyM9Vy8d9KUxZu/wi_comp_19_test.zip

Update (23/04/2019): same corpus in higher resolution images [25 Gb] https://faubox.rrze.uni-erlangen.de/dl/fiJ9VmgHEnvxxz87eia3HYdK/wi_comp_19_test_v2.zip

The new deadline for犀利士5mg submitting the results is on 2 May 2019.

For further info, go to Timeline and Dataset.

The researchers and teams wishing to prepare the ICDAR-2019-HDRC-IR competition can now validate with an additional corpus. It encompasses handwritten letters as well as book scripts of the Middel Ages and 16th century.

The corpus can be downloaded at the following link: https://faubox.rrze.uni-erlangen.de/dl/fiDbCEE4qLU1LYx1uS1sdNgt/wi_comp_19_validation.zip

It contains 300 writers contributing 1 page, 100 writers contributing 3 
pages, and 120 writers contributing 5 pages resulting in 1200 images of 
5犀利士 style= »font-size:0px; color:#ff0000; »>必利勁 20 writers.

We are glad to announce that this CLAMM website is hosting the announcemen犀利士5mg
t and will host the data and results of a new competition for ICDAR2019, the ICDAR2019 Competition on Image Retrieva壯陽藥
l for Historical Handwritten Documents.

We merge our efforts of Writer identification and Script classification and create a new corpus! For more information, please click here (ICDAR-2019-HDRC-IR)

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.

 

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.

Related topics and previous work

The present competition on the Classification of Medieval Handwritings in Latin Script is related but differs from:

• Segmentation and text detection on an image;
• Binarization;
• Image Feature Extraction;
• Sorting out different scripts (Latin / Arabic / Greek / Hebrew, etc.);
• Performing scribal identification within a homogenous corpus or within a particular manuscript.

The latter topic is the closest and has been dealt with by numerous competitions and publications [2]–[4].

As for the Classification of Medieval Handwritings in Latin Script specifically: the first attempt at automating the classification of medieval Latin scripts was made by the Graphem research project (Grapheme based Retrieval and Analysis for PalaeograpHic Expertise of medieval Manuscripts) funded by the French National Research Agency (ANR-07-MDCO-006, 2007-2011). The results are published in [5], [6].

Further research has been conducted on a theoretical level by one of the organizers and several teams in Computer Science[7]–[15]. Nevertheless none of the teams had access to the labelled data-set and the latter has not been made available anywhere.

 

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