{"id":1334,"date":"2019-09-16T11:00:28","date_gmt":"2019-09-16T11:00:28","guid":{"rendered":"https:\/\/campus.hesge.ch\/blog-master-is\/?p=1334"},"modified":"2019-10-26T13:07:20","modified_gmt":"2019-10-26T13:07:20","slug":"ex-imagine-ad-litteras-projet-docerisation-dimprimes-latins-du-xvie-siecle","status":"publish","type":"post","link":"https:\/\/campus.hesge.ch\/blog-master-is\/ex-imagine-ad-litteras-projet-docerisation-dimprimes-latins-du-xvie-siecle\/","title":{"rendered":"Ex imagine ad litteras &#8211; projet d\u2019oc\u00e9risation d\u2019imprim\u00e9s latins du XVIe si\u00e8cle"},"content":{"rendered":"<figure id=\"attachment_1335\" class=\"thumbnail wp-caption aligncenter\" style=\"width: 470px\"><img fetchpriority=\"high\" decoding=\"async\" class=\"wp-image-1335 size-full\" src=\"https:\/\/campus.hesge.ch\/blog-master-is\/wp-content\/uploads\/2019\/08\/jaw.jpg\" alt=\"\" width=\"470\" height=\"661\" srcset=\"https:\/\/campus.hesge.ch\/blog-master-is\/wp-content\/uploads\/2019\/08\/jaw.jpg 470w, https:\/\/campus.hesge.ch\/blog-master-is\/wp-content\/uploads\/2019\/08\/jaw-213x300.jpg 213w\" sizes=\"(max-width: 470px) 100vw, 470px\" \/><figcaption class=\"caption wp-caption-text\"><em>Figure 1: Collection de Bry, livre I, page 13. Frontispice grav\u00e9.<\/em><\/figcaption><\/figure>\n<h2><em>De texto et contexto<\/em> \u2013 le projet en bref<\/h2>\n<p>Notre projet s\u2019inscrit dans une d\u00e9marche de mise en valeur des collections de la <a href=\"https:\/\/fondationbodmer.ch\/bibliotheque__trashed\/collections\/\"><em>Bibliotheca Bodmeriana<\/em><\/a> par le <a href=\"https:\/\/bodmerlab.unige.ch\/fr\">Bodmer Lab<\/a>, un projet impliquant l&#8217;Universit\u00e9 de Gen\u00e8ve et la Fondation Martin Bodmer. Notre travail consiste \u00e0 \u00ab oc\u00e9riser \u00bb les 29 volumes de la <a href=\"https:\/\/bodmerlab.unige.ch\/fr\/constellations\/de-bry\">constellation de Bry<\/a>, dont les num\u00e9risations \u2013 \u00e0 l\u2019image des figures 1 et 4 \u2013 sont d\u2019ores et d\u00e9j\u00e0 accessibles en ligne, et ce afin de rendre leur contenu textuel lisible et explorable par la recherche plein texte. Le but de ce billet est de pr\u00e9senter le processus d\u2019oc\u00e9risation afin d\u2019expliciter la mani\u00e8re dont nos donn\u00e9es de travail seront cr\u00e9\u00e9es et utilis\u00e9es.<\/p>\n<h2><em>De transmutatione <\/em>\u2013 l\u2019art de transformer les images en mots<\/h2>\n<p>Malgr\u00e9 ce titre \u00e9vocateur, il ne sera pas question ici d\u2019alchimie, mais bien de <a href=\"https:\/\/fr.wikipedia.org\/wiki\/Reconnaissance_optique_de_caract%C3%A8res\">reconnaissance optique de caract\u00e8res<\/a>, <em>optical character recognition<\/em> en anglais, d\u2019o\u00f9 l\u2019acronyme OCR \u00e0 l\u2019origine du n\u00e9ologisme \u00ab oc\u00e9risation \u00bb. Un tel processus implique trois types de donn\u00e9es : les<em> inputs<\/em>, des num\u00e9risations aux formats image (jpeg ou tiff, par exemple), les <em>outputs<\/em>, des donn\u00e9es textuelles aux formats txt, xml ou encore <a href=\"https:\/\/www.loc.gov\/standards\/alto\/techcenter\/elementSet\/index.html\">ALTO<\/a>, et enfin du code informatique permettant d\u2019obtenir ces outputs. L\u2019oc\u00e9risation consiste donc \u00e0 identifier des caract\u00e8res dans un document num\u00e9ris\u00e9 et \u00e0 les extraire sous forme de cha\u00eenes de caract\u00e8res lisibles par un humain comme par une machine\u2026 et ce de mani\u00e8re totalement automatis\u00e9e.<\/p>\n<h2><em>De computatione pixelorum<\/em> \u2013 combien de pixels pour un caract\u00e8re ?<\/h2>\n<h2><\/h2>\n<figure id=\"attachment_1336\" class=\"thumbnail wp-caption alignleft\" style=\"width: 150px\"><img decoding=\"async\" class=\"wp-image-1336 size-thumbnail\" src=\"https:\/\/campus.hesge.ch\/blog-master-is\/wp-content\/uploads\/2019\/08\/hs-150x150.png\" alt=\"\" width=\"150\" height=\"150\" \/><figcaption class=\"caption wp-caption-text\"><em>Figure 2 : caract\u00e8re binaris\u00e9 (Anugrah, Bayu et Bintoro 2017)<\/em><\/figcaption><\/figure>\n<p>Derri\u00e8re ce processus a priori simple se cachent des m\u00e9canismes plus complexes, qui se d\u00e9composent en quatre temps (Blanke, Bryant, Hedges 2012) : Le pr\u00e9-traitement ou <em>pre-processing<\/em>, la segmentation ou <em>layout analysis<\/em>, la reconnaissance ou <em>recognition<\/em> et le post-traitement ou <em>post-processing<\/em>.<\/p>\n<figure id=\"attachment_1337\" class=\"thumbnail wp-caption alignright\" style=\"width: 404px\"><img decoding=\"async\" class=\"wp-image-1337 size-full\" src=\"https:\/\/campus.hesge.ch\/blog-master-is\/wp-content\/uploads\/2019\/08\/fl.png\" alt=\"\" width=\"404\" height=\"272\" srcset=\"https:\/\/campus.hesge.ch\/blog-master-is\/wp-content\/uploads\/2019\/08\/fl.png 404w, https:\/\/campus.hesge.ch\/blog-master-is\/wp-content\/uploads\/2019\/08\/fl-300x202.png 300w\" sizes=\"(max-width: 404px) 100vw, 404px\" \/><figcaption class=\"caption wp-caption-text\"><em>Figure 3 : Texte segment\u00e9 (Brener, Iyengar et Pianykh 2005)<\/em><\/figcaption><\/figure>\n<p>Le pr\u00e9-traitement vise \u00e0 rendre la num\u00e9risation manipulable par une machine en se basant sur le traitement des pixels. Lors de cette \u00e9tape, les pixels sont convertis en donn\u00e9es binaires afin de d\u00e9terminer o\u00f9 se trouve le texte sur la page. Cette \u00e9tape permet aussi d\u2019identifier et d\u2019\u00e9liminer le bruit ainsi que les \u00e9l\u00e9ments non-textuels (dessins, gravures, etc.).<\/p>\n<p>La segmentation se base \u00e9galement sur le traitement des pixels, et permet de les diviser en paragraphes puis en lignes de texte en identifiant les marges, les espaces entre les lignes etc.<\/p>\n<h2><\/h2>\n<h2 style=\"text-align: left\"><em>De lingua et machina <\/em>\u2013 mod\u00e8les de langue et intelligence artificielle<\/h2>\n<p>La reconnaissance commence par le d\u00e9coupage des segments en caract\u00e8res. Ce processus peut se faire sur la base des pixels, mais il peut aussi n\u00e9cessiter l\u2019intervention de mod\u00e8les de langues et de caract\u00e8res avec lesquels l\u2019OCR aura \u00e9t\u00e9 entra\u00een\u00e9 au pr\u00e9alable. En effet, cette \u00e9tape fait le plus souvent appel \u00e0 de l\u2019<a href=\"https:\/\/fr.wikipedia.org\/wiki\/Apprentissage_supervis%C3%A9\">apprentissage supervis\u00e9<\/a>, consistant \u00e0 entra\u00eener l\u2019OCR en amont avec des donn\u00e9es du m\u00eame type (textes num\u00e9ris\u00e9s), en lui indiquant o\u00f9 se trouve chaque caract\u00e8re et quelle signe alphanum\u00e9rique il repr\u00e9sente. Sur cette base, l\u2019OCR est cens\u00e9 pouvoir reconna\u00eetre des caract\u00e9ristiques, ou <em>features<\/em>, lui permettant de distinguer les diff\u00e9rents caract\u00e8res et de faire une pr\u00e9diction pour chacun d\u2019entre eux.<\/p>\n<p>Enfin, le post-traitement implique la correction de la transcription propos\u00e9e par l\u2019OCR. Ces corrections peuvent \u00eatre manuelles, mais elles sont de plus en plus automatis\u00e9es, et font intervenir des strat\u00e9gies tr\u00e8s diverses, comme par exemple l\u2019intelligence artificielle (Ptucha et al. 2019) ou le <em>crowdsourcing<\/em> par le jeu (Seidman et al. 2016).<\/p>\n<h2><em>De gratuitis instrumentis<\/em> \u2013 la d\u00e9mocratisation de l\u2019oc\u00e9risation\u2026 et ses limites.<\/h2>\n<p>Le processus ci-dessus et les outils informatiques permettant de le r\u00e9aliser ont \u00e9t\u00e9 d\u00e9velopp\u00e9s au d\u00e9but du XXe si\u00e8cle et sont en constante \u00e9volution depuis (Nagy 2016). Cependant, les premiers logiciels d\u2019oc\u00e9risation sur le march\u00e9 \u00e9taient des \u00ab bo\u00eetes noires \u00bb (Blanke, Bryant, Hedges 2012). Ce n\u2019est qu\u2019avec l\u2019arriv\u00e9e du logiciel <em>open source<\/em> <a href=\"https:\/\/github.com\/tesseract-ocr\/tesseract\">Tesseract<\/a> en 2005 que la magie de l\u2019oc\u00e9risation a pu \u00eatre d\u00e9voil\u00e9e, ouvrant ainsi la voie \u00e0 la cr\u00e9ation de nombreux autres OCR libres.<\/p>\n<p>Ces OCR sont g\u00e9n\u00e9ralement d\u00e9j\u00e0 entra\u00een\u00e9s \u00e0 l\u2019aide de num\u00e9risations, de mod\u00e8les de langues et de polices de caract\u00e8res. Cela a pour avantage de les rendre \u00ab pr\u00eats \u00e0 l\u2019emploi \u00bb, mais pr\u00e9sente l\u2019inconv\u00e9nient de limiter leurs comp\u00e9tences selon les <em>inputs<\/em> : tous les OCR ne sont pas efficaces de mani\u00e8re \u00e9quivalente avec toutes les langues, toutes les polices de caract\u00e8res et toutes les qualit\u00e9s d\u2019image.<\/p>\n<h2><em>Quem ad finem ?<\/em> \u2013 De Bry oc\u00e9ris\u00e9<\/h2>\n<p>Notre projet consiste donc \u00e0 tester diff\u00e9rents OCR <em>open source<\/em> afin de d\u00e9terminer lequel sera le plus performant dans le traitement des num\u00e9risations \u00e0 notre disposition. L\u2019OCR retenu servira ensuite \u00e0 transcrire l\u2019int\u00e9gralit\u00e9 de la collection de Bry, et nous devrons alors proposer des solutions de traitement efficaces \u2013 adaptation du code, entra\u00eenement sur de nouveaux mod\u00e8les, d\u00e9veloppement de<a href=\"https:\/\/fr.wikipedia.org\/wiki\/R%C3%A9seau_de_neurones_artificiels\"> r\u00e9seaux neuronaux artificiels<\/a> etc. \u2013 afin d\u2019obtenir une transcription automatique pr\u00e9sentant un taux d\u2019erreur minimal. Cette transcription pourra ensuite \u00eatre utilis\u00e9e sur le site du Bodmer Lab pour faciliter l\u2019acc\u00e8s au texte et la recherche dans les 25 volumes de la collection.<\/p>\n<p>Bien que l\u2019oc\u00e9risation soit une technologie mature, ce projet n\u2019implique pas de r\u00e9inventer la roue, mais bien d\u2019utiliser et d\u2019optimiser des technologies d\u00e9j\u00e0 \u00e9prouv\u00e9es pour \u00e9valuer leur performance face aux difficult\u00e9s que peuvent pr\u00e9senter des imprim\u00e9s latins du XVIe si\u00e8cle.<\/p>\n<figure id=\"attachment_1338\" class=\"thumbnail wp-caption aligncenter\" style=\"width: 1024px\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-1338 size-large\" src=\"https:\/\/campus.hesge.ch\/blog-master-is\/wp-content\/uploads\/2019\/08\/gfe-1024x683.jpg\" alt=\"\" width=\"1024\" height=\"683\" srcset=\"https:\/\/campus.hesge.ch\/blog-master-is\/wp-content\/uploads\/2019\/08\/gfe-1024x683.jpg 1024w, https:\/\/campus.hesge.ch\/blog-master-is\/wp-content\/uploads\/2019\/08\/gfe-300x200.jpg 300w, https:\/\/campus.hesge.ch\/blog-master-is\/wp-content\/uploads\/2019\/08\/gfe-768x512.jpg 768w, https:\/\/campus.hesge.ch\/blog-master-is\/wp-content\/uploads\/2019\/08\/gfe.jpg 1045w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"caption wp-caption-text\"><em>Figure 4 : Collection de Bry, livre 11, page 17. Carte de la Nouvelle-Guin\u00e9e.<\/em><\/figcaption><\/figure>\n<h2>Bibliographie<\/h2>\n<p>ANUGRAH, Rio et BINTORO, Ketut Bayu Yogha, 2017. Latin letters recognition using optical character recognition to convert printed media into digital format. <em>Jurnal Elektronika Dan Telekomunikasi<\/em> [en ligne]. D\u00e9cembre 2017. Vol. 17, n\u00b02, pp. 56\u201362. [Consult\u00e9 le 28 juin 2019]. Disponible \u00e0 l\u2019adresse : <a href=\"https:\/\/www.jurnalet.com\/jet\/article\/view\/163\">https:\/\/www.jurnalet.com\/jet\/article\/view\/163<\/a><\/p>\n<p>BLANKE, Tobias, BRYANT, Michael et HEDGES, Mark, 2012. Open source optical character recognition for historical research. <em>Journal of Documentation<\/em> [en ligne]. Ao\u00fbt 2012. Vol. 68, n\u00b05, pp. 659\u2013683. [Consult\u00e9 le 28 juin 2019]. Disponible \u00e0 l\u2019adresse : <a href=\"https:\/\/www.emeraldinsight.com\/doi\/full\/10.1108\/00220411211256021\">https:\/\/www.emeraldinsight.com\/doi\/full\/10.1108\/00220411211256021<\/a><\/p>\n<p>BRENER, Nathan E., IYENGAR, S. S. et PIANYKH, O. S., 2005. A conclusive methodology for rating OCR performance. <em>Journal of the American Society for Information Science &amp; Technology<\/em> [en ligne]. Juillet 2005. Vol. 56, n\u00b012, pp. 1274\u20131287. [Consult\u00e9 le 28 juin 2019]. Disponible \u00e0 l\u2019adresse : <a href=\"http:\/\/search.ebscohost.com\/login.aspx?direct=true&amp;db=lih&amp;AN=18172083&amp;site=ehost-live\">http:\/\/search.ebscohost.com\/login.aspx?direct=true&amp;db=lih&amp;AN=18172083&amp;site=ehost-live<\/a><\/p>\n<p>NAGY, Georges, 2016. Disruptive developments in document recognition. <em>Pattern Recognition Letters<\/em> [en ligne]. Ao\u00fbt 2016. Vol. 79, pp. 106\u2013112. [Consult\u00e9 le 28 juin 2019]. Disponible \u00e0 l\u2019adresse : <a href=\"http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167865515004109\">http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167865515004109<\/a><\/p>\n<p>PTUCHA, Raymond, et al., 2019. Intelligent character recognition using fully convolutional neural networks. <em>Pattern Recognition<\/em> [en ligne]. Avril 2019. Vol. 88, pp. 604\u2013613. [Consult\u00e9 le 28 juin 2019]. Disponible \u00e0 l\u2019adresse : <a href=\"http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0031320318304370\">http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0031320318304370<\/a><\/p>\n<p>SEIDMAN, Max J., et al., 2016. Are games a viable solution to crowdsourcing improvements to faulty OCR ? &#8211; The Purposeful Gaming and BHL experience. <em>Code4Lib Journal<\/em> [en ligne]. Juillet 2016. Vol. 33, p. 1. [Consult\u00e9 le 28 juin 2019]. Disponible \u00e0 l\u2019adresse : <a href=\"http:\/\/search.ebscohost.com\/login.aspx?direct=true&amp;db=lih&amp;AN=116963678&amp;site=ehost-live\">http:\/\/search.ebscohost.com\/login.aspx?direct=true&amp;db=lih&amp;AN=116963678&amp;site=ehost-live<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>De texto et contexto \u2013 le projet en bref Notre projet s\u2019inscrit dans une d\u00e9marche de mise en valeur des collections de la Bibliotheca Bodmeriana par le Bodmer Lab, un projet impliquant l&#8217;Universit\u00e9 de Gen\u00e8ve et la Fondation Martin Bodmer. &hellip; <a href=\"https:\/\/campus.hesge.ch\/blog-master-is\/ex-imagine-ad-litteras-projet-docerisation-dimprimes-latins-du-xvie-siecle\/\">Lire la suite\u00ad\u00ad<\/a><\/p>\n","protected":false},"author":15,"featured_media":1335,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18,15,19,12],"tags":[58,163,66,169,32,166,52,167,50,30,165,164,31,168],"class_list":["post-1334","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-advanced-neural-net","category-information-donnees","category-machine-learning","category-reflexion-is","tag-advanced-neural-network","tag-bodmer-lab","tag-collaboration","tag-fondation-bodmer","tag-heg-geneve","tag-imprimes-anciens","tag-intelligence-artificielle","tag-latin","tag-machine-learning","tag-master-is","tag-ocerisation","tag-ocr","tag-sciences-de-linformation","tag-tesseract"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Ex imagine ad litteras - projet d\u2019oc\u00e9risation d\u2019imprim\u00e9s latins du XVIe si\u00e8cle - Recherche d&#039;Id\u00e9eS<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/campus.hesge.ch\/blog-master-is\/ex-imagine-ad-litteras-projet-docerisation-dimprimes-latins-du-xvie-siecle\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Ex imagine ad litteras - projet d\u2019oc\u00e9risation d\u2019imprim\u00e9s latins du XVIe si\u00e8cle - Recherche d&#039;Id\u00e9eS\" \/>\n<meta property=\"og:description\" content=\"De texto et contexto \u2013 le projet en bref Notre projet s\u2019inscrit dans une d\u00e9marche de mise en valeur des collections de la Bibliotheca Bodmeriana par le Bodmer Lab, un projet impliquant l&#8217;Universit\u00e9 de Gen\u00e8ve et la Fondation Martin Bodmer. &hellip; 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