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Quaero Technology Catalog

Nabil Bouzerna
Nabil Bouzerna
Nabil BouzernaChief Technology Officer at Arkangel, Paris-Saclay

Quaero , the first research and innovation cluster on multimedia and multilingual content processing. The Technology Catalog presents 72 modules or demonstrators, each of them being described in a double page which provides details on the application domain and technical characteristics. It is composed of two parts: - 59 Core Technology Modules, organized per thematic domains; the list of 12 domains, provided on p.4, is reminded on each left-hand page - 13 Application Demonstrators; their list, provided on p.5, is reminded on each left-hand page The catalog can also be searched by institution using the index provided at the end of the document.

Quaero Technology Catalog

1 of 180
TechnologyCatalog
quaero-catalogue-210x210-v1.6-page-per-page.indd 1 02/10/2013 09:53:32
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CatalogueTechnologique
quaero-catalogue-210x210-v2-dernieres-pages.indd 1 02/10/2013 12:47:23
Quaero, premier pôle de recherche et
d’innovation sur les technologies de traitement
automatique des contenus multimédias et multilingues
À l’origine de Quaero, il y a le besoin de fédérer et renforcer
une filière technologique émergente, celle du traitement
sémantique des contenus multimédias et multilingues (texte,
parole, musique, images fixes, vidéo, documents numérisés).
Il y a aussi la volonté de se comparer régulièrement à l’état de
l’art international, d’organiser la chaîne complète de transfert
technologique, et de mobiliser les acteurs de cette filière
autour d’applications correspondant à des marchés identifiés
comme importants, tels que les moteurs de recherche, la
télévision personnalisée et la numérisation du patrimoine
culturel.
Ceprogrammeestportéparunconsortiumde32partenaires
publics et privés, français et allemands. Pendant la phase de
R&D, de 2008 à 2013, ces partenaires ont produit des corpus,
effectué des recherches sur un large spectre scientifique,
développé et testé des modèles de plus en plus élaborés,
partagéleurexpérience,intégréleurslogiciels. Leprogramme
de travail a été adapté aux évolutions du contexte. De
nouvelles applications sont apparues, comme la gestion
du courrier entrant en entreprise ou l’aide à la création de
site web multilingues, et les efforts sur les technologies
correspondantesontétérenforcés,commelareconnaissance
d’écriture ou la traduction automatique. Les collaborations,
notamment entre disciplines et entre laboratoires de
recherche et entreprises, se sont approfondies.
Fruit de ces travaux, qui ont donné lieu à plus de
800publicationsnationalesetinternationalesetde
nombreusesdistinctions,unecentainedemodules
technologiques et démonstrateurs applicatifs ont
été développés, dont certains commencent déjà
à être exploités commercialement. Un grand
nombre d’entre eux présente un intérêt au-delà
des membres du consortium. Ils font l’objet du
présent catalogue.
Ce catalogue technologique présente 72
modules ou démonstrateurs décrits chacun
dans une double page qui en précise le
domaine d’application et les caractéristiques
techniques. Il est composé de deux parties :
59•	 modules technologiques, organisés
par domaine thématique ; la liste des 12
domaines, qui apparaît p. 4, est rappelée
sur chaque page de gauche
13•	 démonstrateurs applicatifs ; la liste,
qui apparaît p. 5, est rappelée sur chaque
page de gauche
Le lecteur pourra également effectuer une
recherche par partenaire à partir de l’index
en fin de document
quaero-catalogue-210x210-v1.6-page-per-page.indd 4 02/10/2013 09:53:32
The Quaero program stems from the need to federate and
strengthen an emerging technological sector, dealing with
the semantic processing of multimedia and multilingual
content (text, speech, music, still and video image, scanned
documents). It also arises from the will to systematically
benchmark results against international standards, to
organize a complete technological transfer value chain, and
to mobilize the actors of the sector around applications
corresponding to identified and potentially large markets,
suchassearchengines,personalizedTV,andthedigitization
of cultural heritage.
Quaero, the first research and innovation cluster
on multimedia and multilingual content processing
This program is borne by a consortium of 32
FrenchandGermanpartnersfromthepublicand
privatesector. DuringtheR&Dphase,from2008
to 2013, these partners have produced corpora,
performed research covering a large scientific
spectrum, developed and tested increasingly
elaborate models, shared experience, integrated
software. The work plan has been adapted
to the context evolutions. New applications
have appeared, such as the management of
professional incoming mail or computer-aided
multilingual web site creation, and additional
efforts have been put on the corresponding
technologies, such as handwriting recognition
or machine translation. Collaboration became
more extensive, especially across disciplines and
between research and industry.
Thanks to these efforts, which led to more than
800 national and international publications and
to numerous distinctions, about one hundred
core technology modules and application
demonstrators have been developed, some
of them being already commercially exploited.
Many of these technologies are of interest
beyond the consortium members. Presenting
them is the purpose of this catalog.
The Technology Catalog presents 72 modules or
demonstrators, each of them being described in a
double page which provides details on the application
domain and technical characteristics. It is composed of
two parts:
59•	 Core Technology Modules, organized per
thematic domains; the list of 12 domains, provided on
p.4, is reminded on each left-hand page
13•	 Application Demonstrators; their list, provided
on p.5, is reminded on each left-hand page
The catalog can also be searched by institution using the
index provided at the end of the document.
quaero-catalogue-210x210-v1.6-page-per-page.indd 5 02/10/2013 09:53:32
Semantic Acquisition &
Annotation (5) p8 to 17
Q&A
(4) p20 to 27
Translation of Text
and Speech (2) p46 to 49
Speech
Processing
(7) p30 to 43
Document Processing
(10) p60 to 79
Audio
Processing
(3) p52 to 57
Object Recognition & Image
Clustering (3) p82 to 87
Music
Processing
(7) p90 to 103
Indexing, Ranking
and Retrieval (1) p106 to 107
Content
Analysis
(4) p110 to 117
Video Analysis & Structuring
(12) p124 to 147
Gesture
Recognition
(1) p120 to 121
Core Technology Modules
4
quaero-catalogue-210x210-v1.6-page-per-page.indd 4 02/10/2013 09:53:32

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Quaero Technology Catalog

  • 4. Quaero, premier pôle de recherche et d’innovation sur les technologies de traitement automatique des contenus multimédias et multilingues À l’origine de Quaero, il y a le besoin de fédérer et renforcer une filière technologique émergente, celle du traitement sémantique des contenus multimédias et multilingues (texte, parole, musique, images fixes, vidéo, documents numérisés). Il y a aussi la volonté de se comparer régulièrement à l’état de l’art international, d’organiser la chaîne complète de transfert technologique, et de mobiliser les acteurs de cette filière autour d’applications correspondant à des marchés identifiés comme importants, tels que les moteurs de recherche, la télévision personnalisée et la numérisation du patrimoine culturel. Ceprogrammeestportéparunconsortiumde32partenaires publics et privés, français et allemands. Pendant la phase de R&D, de 2008 à 2013, ces partenaires ont produit des corpus, effectué des recherches sur un large spectre scientifique, développé et testé des modèles de plus en plus élaborés, partagéleurexpérience,intégréleurslogiciels. Leprogramme de travail a été adapté aux évolutions du contexte. De nouvelles applications sont apparues, comme la gestion du courrier entrant en entreprise ou l’aide à la création de site web multilingues, et les efforts sur les technologies correspondantesontétérenforcés,commelareconnaissance d’écriture ou la traduction automatique. Les collaborations, notamment entre disciplines et entre laboratoires de recherche et entreprises, se sont approfondies. Fruit de ces travaux, qui ont donné lieu à plus de 800publicationsnationalesetinternationalesetde nombreusesdistinctions,unecentainedemodules technologiques et démonstrateurs applicatifs ont été développés, dont certains commencent déjà à être exploités commercialement. Un grand nombre d’entre eux présente un intérêt au-delà des membres du consortium. Ils font l’objet du présent catalogue. Ce catalogue technologique présente 72 modules ou démonstrateurs décrits chacun dans une double page qui en précise le domaine d’application et les caractéristiques techniques. Il est composé de deux parties : 59• modules technologiques, organisés par domaine thématique ; la liste des 12 domaines, qui apparaît p. 4, est rappelée sur chaque page de gauche 13• démonstrateurs applicatifs ; la liste, qui apparaît p. 5, est rappelée sur chaque page de gauche Le lecteur pourra également effectuer une recherche par partenaire à partir de l’index en fin de document quaero-catalogue-210x210-v1.6-page-per-page.indd 4 02/10/2013 09:53:32
  • 5. The Quaero program stems from the need to federate and strengthen an emerging technological sector, dealing with the semantic processing of multimedia and multilingual content (text, speech, music, still and video image, scanned documents). It also arises from the will to systematically benchmark results against international standards, to organize a complete technological transfer value chain, and to mobilize the actors of the sector around applications corresponding to identified and potentially large markets, suchassearchengines,personalizedTV,andthedigitization of cultural heritage. Quaero, the first research and innovation cluster on multimedia and multilingual content processing This program is borne by a consortium of 32 FrenchandGermanpartnersfromthepublicand privatesector. DuringtheR&Dphase,from2008 to 2013, these partners have produced corpora, performed research covering a large scientific spectrum, developed and tested increasingly elaborate models, shared experience, integrated software. The work plan has been adapted to the context evolutions. New applications have appeared, such as the management of professional incoming mail or computer-aided multilingual web site creation, and additional efforts have been put on the corresponding technologies, such as handwriting recognition or machine translation. Collaboration became more extensive, especially across disciplines and between research and industry. Thanks to these efforts, which led to more than 800 national and international publications and to numerous distinctions, about one hundred core technology modules and application demonstrators have been developed, some of them being already commercially exploited. Many of these technologies are of interest beyond the consortium members. Presenting them is the purpose of this catalog. The Technology Catalog presents 72 modules or demonstrators, each of them being described in a double page which provides details on the application domain and technical characteristics. It is composed of two parts: 59• Core Technology Modules, organized per thematic domains; the list of 12 domains, provided on p.4, is reminded on each left-hand page 13• Application Demonstrators; their list, provided on p.5, is reminded on each left-hand page The catalog can also be searched by institution using the index provided at the end of the document. quaero-catalogue-210x210-v1.6-page-per-page.indd 5 02/10/2013 09:53:32
  • 6. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 4 quaero-catalogue-210x210-v1.6-page-per-page.indd 4 02/10/2013 09:53:32
  • 7. Application Demonstrators Chromatik - p148 MediaCentric® - p152 MuMa: The Music Mashup - p158 SYSTRANLinks - p166 Voxalead multimedia search engine - p170 MECA: Multimedia Enterprise CApture - p150 MobileSpeech - p156 PlateusNet - p164 MediaSpeech® product line - p154 Personalized and social TV - p162 OMTP: Online Multimedia Translation Platform - p160 Voxalead Débat Public - p168 VoxSigma SaaS - p172 5 quaero-catalogue-210x210-v1.6-page-per-page.indd 5 02/10/2013 09:53:35
  • 8. AlvisAE: Alvis Annotation Editor - Inra p8 KIWI: Keyword extractor - Inria p14 Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 6 quaero-catalogue-210x210-v1.6-page-per-page.indd 6 02/10/2013 09:53:47
  • 9. AlvisIR - Inra p10 TyDI: Terminology Design Interface - Inra p16 Alvis NLP: Alvis Natural Language Processing - Inra p12 7 quaero-catalogue-210x210-v1.6-page-per-page.indd 7 02/10/2013 09:53:47
  • 10. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 8 Alvis Annotation Editor Application sectors Any sector using text documents• Information Extraction• Contents analysis• Target users and customers With AlvisAE, remote users display annotated documents in their web browser and manually create new annotations over the text and share them. Partners: Inra quaero-catalogue-210x210-v1.6-page-per-page.indd 8 02/10/2013 09:53:59
  • 11. 9 AlvisAE: Alvis Annotation Editor Contact details: Robert Bossy robert.bossy@jouy.inra.fr Description: Technical requirements: Conditions for access and use: INRA MIG Domaine de Vilvert 78352 Jouy-en-Josas France http://bibliome.jouy.inra.fr AlvisAE is a Web Annotation Editor designed to display and edit fine-grained semantic formal annotations of textual documents. The annotations are used for fast reading or for training Machine Learning algorithms in text mining. The annotations can also be stored in a database and queried. The annotations are entities, n-ary relations and groups. The entities can be discontinuous and overlapping. They are typed by a small set of categories or by concepts from an external ontology. The user can dynamically extend the ontology by dragging new annotations from the text to the ontology. AlvisAE supports collaborative and concurrent annotations and adjudication. Input documents can be in HTML or text format. AlvisAE takes also as input semantic pre-annotations automatically produced by AlvisNLP. Server side: Java 6 or 7, a Java Application and a RDMS. Client Side: The client application can be run by any recent JavaScript enabled web browser (e.g. Firefox, Chromium, Safari). Internet Explorer is not supported. AlvisAE software is developped by INRA, Mathématique, Informatique et Génome lab. It is property of INRA. AlvisAE can be supplied under licence on a case-by- case basis. An open-source distribution is planned in the short term. quaero-catalogue-210x210-v1.6-page-per-page.indd 9 02/10/2013 09:54:03
  • 12. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 10 Semantic document indexing and search engine framework Target users and customers Domain-specific communities, especially technical and scientific, willing to build search engines and information systems to manage documents with fine- grained semantic annotations. Partners: Inra Application sectors Search engines and information systems development. quaero-catalogue-210x210-v1.6-page-per-page.indd 10 02/10/2013 09:54:15
  • 13. 11 AlvisIR Contact details: Robert Bossy robert.bossy@jouy.inra.fr Description: Technical requirements: Conditions for access and use: INRA MIG Domaine de Vilvert 78352 Jouy-en-Josas cedex France http://bibliome.jouy.inra.fr Linux platform• Perl• libxml2• Zebra indexing engine• PHP5• Sources available upon request. Free of use for academic institutions. AlvisIR is a complete suite for indexing documents with fine-grained semantic annotations. The search engine performs a semantic analysis of the user query and searches for synonyms and sub-concepts. AlvisIR has two main components: 1. the indexing tool and search daemon based on IndexData’s Zebra that supports standard CQL queries, 2. the web user interface featuring result snippets, query-term highlight, facet filtering and concept hierarchy browsing. Setting up a search engine requires the semantic resources for query analysis (synonyms and concept hierarchy) and a set of annotated documents. AlvisIR is closely integrated with AlvisNLP and TyDI for document annotation and semantic resources acquisition respectively. Indicative indexing time: 24mn for a corpus containing 5 million annotations. Indicative response time: 18s for a response containing 20,000 annotations. quaero-catalogue-210x210-v1.6-page-per-page.indd 11 02/10/2013 09:54:19
  • 14. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 12 A pipeline framework for Natural Language Processing Target users and customers The targeted audience includes projects that require usual Natural Language Processing tools for production and research purpose. Partners: Inra Application sectors Natural language processing• Contents analysis• Information retrieval• quaero-catalogue-210x210-v1.6-page-per-page.indd 12 02/10/2013 09:54:32
  • 15. 13 Alvis NLP: Alvis Natural Language Processing Contact details: Robert Bossy robert.bossy@jouy.inra.fr Description: INRA MIG Domaine de Vilvert 78352 Jouy-en-Josas cedex France http://bibliome.jouy.inra.fr Technical requirements: Java 7 Weka Conditions for access and use: Sources available upon request. Free of use for academic institutions. Alvis NLP is a pipeline framework to annotate text documents using Natural Language Processing (NLP) tools for sentence and word segmentation, named-entity recognition, term analysis, semantic typing and relation extraction (see the paper by Nedellec et al. in Handbook on Ontologies 2009 for a comprehensive overview). The various available functions are accessible as modules, that can be composed in a sequence forming the pipeline. This sequence, as well as parameters for the modules, is specified through a XML-based configuration file. New components can easily be integrated into the pipeline. To implement a new module, one has to build a Java class manipulating text annotations following the data model defined in Alvis NLP. The class is loaded at run-time by Alvis NLP, which makes the integration much easier. quaero-catalogue-210x210-v1.6-page-per-page.indd 13 02/10/2013 09:54:34
  • 16. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 14 Keyword extractor Target users and customers The targeted users and customers are the multimedia industry actors, and all academic or industrial laboratories interested in textual document processing. Partners: Inra Application sectors Textual and multimedia document processing• quaero-catalogue-210x210-v1.6-page-per-page.indd 14 02/10/2013 09:54:47
  • 17. 15 KIWI: Keyword extractor Contact details: General issues: Patrick Gros patrick.gros@irisa.fr Description: Technical requirements: Conditions for access and use: IRISA/Texmex team Campus de Beaulieu 35042 Rennes Cedex France http://www.irisa.fr/ SPC with Unix/Linux OS• Kiwi requires the TreeTagger [1] software to be• installed on the system Kiwi requires the Flemm [2] software to be installed on• the system [1] http://www.ims.uni-stuttgart.de/projekte/corplex/ TreeTagger/ [2] http://www.univnancy2.fr/pers/namer/Telecharger_ Flemm.htm Kiwi is a software that has been developed at Irisa/Inria- Rennes and is the property of Inria. Registration at the Agency for Program Protection (APP) in France, is under process. Kiwi is currently available as a prototype only. It can be released and supplied under license on a case-by-case basis. Technical issues: Sébastien Campion scampion@irisa.fr Kiwi is a software dedicated to the extraction of keywords from a textual document. From an input text, or preferably a normalized text, Kiwi outputs a weighted word vector (see figure 1 below). This ranked keyword vector can then be used as a document description or for indexing purposes. Kiwi is a software dedicated to the extraction of keywords from a textual document. From an input text, or preferably a normalized text, Kiwi outputs a weighted word vector (see figure 1 below). This ranked keyword vector can then be used as a document description or for indexing purposes. Kiwi was developed at Irisa/INRIA Rennes by the Texmex team. The Kiwi author is: Gwénolé Lecorvé quaero-catalogue-210x210-v1.6-page-per-page.indd 15 02/10/2013 09:54:52
  • 18. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 16 A platform for the validation, structuration and export of termino- ontologies Target users and customers The primary use of TyDI is the design of termino- ontologies for the indexing of textual documents. It can therefore be of great help for most projects involved in natural language processing. Partners: Inra Application sectors Terminology structuration• Textual document indexing• Natural language processing• quaero-catalogue-210x210-v1.6-page-per-page.indd 16 02/10/2013 09:55:05
  • 19. 17 TyDI: Terminology Design Interface Contact details: Robert Bossy robert.bossy@jouy.inra.fr Description: Technical requirements: Conditions for access and use: INRA MIG Domaine de Vilvert 78352 Jouy-en-Josas Cedex France http://bibliome.jouy.inra.fr Server side: Glassfish and Postgresql servers• Client side: Java Virtual Machine version 1.5• TyDI is a software developped by INRA, Mathématique, Informatique et Génome and is the property of INRA. TyDI can be supplied under licence on a case-by-case basis. For more information, please contact Robert Bossy (robert.bossy@jouy.inra.fr) Figure 1: The client interface of TyDI. It is composed of several panels (hierarchichal/tabular view of the terms, search panel, context of appearance of selected terms …) TyDI is a collaborative tool for manual validation/ annotation of terms either originating from terminologies or extracted from training corpus of textual documents. It is used on the output of so-called term extractor programs (like Yatea), which are used to identify candidate terms (e.g. compound nouns). Thanks to TyDI, a user can validate candidate terms and specify synonymy/hyperonymy relations. These annotations can then be exported in several formats, and used in other Natural Language Processing tools. quaero-catalogue-210x210-v1.6-page-per-page.indd 17 02/10/2013 09:55:12
  • 20. FIDJI:Web Question-Answering System - LIMSI - CNRS p20 RITEL: Spoken and Interactive Question-Answering System - LIMSI - CNRS p26 Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 18 quaero-catalogue-210x210-v1.6-page-per-page.indd 18 02/10/2013 09:55:24
  • 21. Question-Answering System - Synapse Développement p22 QAVAL: Question Answering by Validation - LIMSI - CNRS p24 19 quaero-catalogue-210x210-v1.6-page-per-page.indd 19 02/10/2013 09:55:24
  • 22. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 20 A question-answering system aims at answering questions written in natural language with a precise answer. Target users and customers Web Question-answering is an end-user application. FIDJI is an open-domain QA system for French and English Partners: LIMSI-CNRS Application sectors Information retrieval on the Web or in document collections quaero-catalogue-210x210-v1.6-page-per-page.indd 20 02/10/2013 09:55:37
  • 23. 21 FIDJI: Web Question-Answering System Contact details: Véronique Moriceau moriceau@limsi.fr Description: LIMSI-CNRS Groupe ILES B.P. 133 91403 Orsay Cedex France http://www.limsi.fr/ Technical requirements: PC with Linux platform Conditions for access and use: Available for licensing on case-by-case basis Xavier Tannier xtannier@limsi.fr Document retrieval systems such as search engines provide the user with a large set of pairs URL/snippets containing relevant information with respect to a query. To obtain a precise answer, the user then needs to locate relevant information within the documents and possibly to combine different pieces of information coming from one or several documents. To avoid these problems, focused retrieval aims at identifying relevant documents and locating the precise answer to a user question within a document. Question- answering (QA) is a type of focused retrieval: its goal is to provide the user with a precise answer to a natural language question. While information retrieval (IR) methods are mostly numerical and use only little linguistic knowledge, QA often implies deep linguistic processing, large resources and expert rule-based modules. Most question-answering systems can extract the answer to a factoid question when it is explicitly present in texts, but are not able to combine different pieces of information to produce an answer. FIDJI (Finding In Documents Justifications and Inferences), an open- domain QA system for French and English, aims at going beyond this insufficiency and focuses on introducing text understanding mechanisms. The objective is to produce answers which are fully validated by a supporting text (or passage) with respect to a given question. The main difficulty is that an answer (or some pieces of information composing an answer) may be validated by several documents. For example: Q: Which French Prime Minister committed suicide? A: Pierre Bérégovoy P1: The French Prime Minister Pierre Bérégovoy warned Mr. Clinton against… P2: Two years later, Pierre Bérégovoy committed suicide after he was indirectly implicated… In this example, the information “French Prime Minister” and “committed suicide” are validated by two different complementary passages. Indeed, this question may be decomposed into two sub-questions, e.g. “Who committed suicide?” and “Are they French Prime Minister?”. FIDJI uses syntactic information, especially dependency relations which allow question decomposition. The goal is to match the dependency relations derived from the question and those of a passage and to validate the type of the potential answer in this passage or in another document. Another important aim of FIDJI is to answer new categories of questions, called complex questions, typically “how” and “why” questions. Complex questions do not exist in traditional evaluation campaigns but have been introduced within the Quaero framework. Answers to these particular questions are no longer short and precise answers, but rather parts of documents or even full documents. In this case, the linguistic analysis of the question provides a lot of information concerning the possible form of the answer and keywords that should be sought in candidate passages. quaero-catalogue-210x210-v1.6-page-per-page.indd 21 02/10/2013 09:55:37
  • 24. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 22 A Question Answering system allows the user to ask questions in natural language and to obtain one or several answers. For boolean and generic questions, our system is able to generate potential questions and to return the corresponding answers. Target users and customers End-user application, Question-Answering is the easiest way to find information for everybody: ask the question as you want and obtain answers, not snippets or pages. Partners: Synapse Développement Application sectors Search and find precise answers in any collection of texts, from the Web or any other source (voice recognition, optical character recognition, etc.), with eventual correction of the source text, ability to generate questions from generic requests, eventually a single word, ability to find similar questions and their answers, etc. Monolingual and multilingual Question-Answering system. Languages: English, French (+ Spanish, Portuguese, Polish, with partners using the same API). quaero-catalogue-210x210-v1.6-page-per-page.indd 22 02/10/2013 09:55:50
  • 25. 23 Question-Answering System Contact details: Patrick Séguéla patrick.seguela@synapse-fr.com Description: Technical requirements: Conditions for access and use: Synapse Développement 33, rue Maynard 31000 Toulouse France http://www.synapse-developpement.fr/ SPC with Windows or Linux• RAM minimum : 4 Gb• HDD minimum : 100 Gb• SDK available for integration in programs or Web services. For specific conditions of use, contact us. The technology is a system based on very consequent linguistic resources and on NLP state- of-the-art technologies, especially, syntactic and semantic parsing, with sophisticated features like resolution of anaphora, word sense disambiguation or relations between named entities. On news and Web corpora, our system is regularly awarded in the international and national evaluation campaigns (EQueR 2004, CLEF 2005, 2006, 2007, Quaero 2008, 2009). quaero-catalogue-210x210-v1.6-page-per-page.indd 23 02/10/2013 09:55:55
  • 26. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 24 A question answering system that is adapted for searching precise answers in textual passages extracted from Web documents or text collections. Target users and customers Question-answering is for both the general public to retrieve precise information in raw texts, and for companies and organizations, that have specific text mining needs. Question-answering systems suggest short answers and their justification passage to questions provided in natural language. Partners: LIMSI-CNRS Application sectors Extension of search engine, technology monitoring quaero-catalogue-210x210-v1.6-page-per-page.indd 24 02/10/2013 09:56:08
  • 27. 25 QAVAL: Question Answering by VALidation Contact details: Brigitte Grau Brigitte.Grau@limsi.fr Technical requirements: Conditions for access and use: LIMSI-CNRS ILES Group B.P. 133 91403 Orsay Cedex France www.limsi.fr/Scientifique/iles Linux platform Available for licensing on a case-by-case basis Description: The large number of documents currently on the Web, but also on intranet systems, makes it necessary to provide users with intelligent assistant tools to help them find the specific information they are searching for. Relevant information at the right time can help solving a particular task. Thus, the purpose is to be able to access the content of texts, and not only give access to documents. Question-answering systems address this question. Question-answering systems aim at finding answers to a question asked in natural language, using a collection of documents. When the collection is extracted from the Web, the structure and style of the texts are quite different from those of newspaper articles. We developed a question-answering system QAVAL based on an answer validation process able to handle both kinds of documents. A large number of candidate answers are extracted from short passages in order to be validated, according to question and excerpt characteristics. The validation module is based on a machine learning approach. It takes into account criteria characterizing both excerpt and answer relevance at surface, lexical, syntactic and semantic levels, in order to deal with different types of texts. QAVAL is made of sequential modules, corresponding to five main steps. The question analysis provides main characteristics to retrieve excerpts and guide the validation process. Short excerpts are obtained directly from the search engine and are parsed and enriched with the question characteristics, which allows QAVAL to compute the different features for validating or discarding candidate answers. quaero-catalogue-210x210-v1.6-page-per-page.indd 25 02/10/2013 09:56:15
  • 28. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 26 A spoken and interactive QA system that helps a user to find an answer to his question, spoken or written, in a collection of documents. Target users and customers Question-answering is an end-user application. The purpose is to go beyond the traditional way of retrieving information through search engines. Our system is interactive, with both a speech (phone or microphone) and text (web) interface. Partners: LIMSI-CNRS Application sectors QA system can be viewed as a direct extension of search engines. They allow a user to ask questions in natural language. quaero-catalogue-210x210-v1.6-page-per-page.indd 26 02/10/2013 09:56:28
  • 29. 27 RITEL: Spoken and Interactive Question-Answering System Contact details: Sophie Rosset sophie.rosset@limsi.fr Description: Technical requirements: Conditions for access and use: LIMSI-CNRS TLP Group B.P. 133 91403 Orsay Cedex France http://www.limsi.fr/tlp/ PC with Linux platform. Available for licensing on case-by-case basis. There are different ways to go beyond standard retrieval systems such as search engines. One of them is to offer the users different ways to express their query: some prefer to use speech to express a query, while others prefer written natural language. Another way is to allow the user to interact with the system. The Ritel system aims at integrating a dialog system and an open-domain information retrieval system to allow a human to ask a general question (f.i. « Who is currently presiding the Senate?’’ or « How did the price of gas change for the last ten years?’’) and refine his research interactively. A human-computer dialog system analyses and acts on the user requests depending on the task at hand, the previous interactions and the user’s behaviour. Its aim is to provide the user with the information being sought while maintaining a smooth and natural interaction flow. The following example illustrates the kind of possible interaction with the Ritel system: [S] Hi, Ritel speaking! What is your first question? [U] who built the Versailles Castle [S] Your search is about Versailles Castle and built . The answer is Louis XIII. Do you want to ask another question? [U] in which year [S] 1682, according to the documents I had access to. Another question? [U] Who designed the garden [S] The following items are used for searching: Versailles, gardens and designed. André Le Nôtre. Anything else? The dialog system is comprised of a component for user utterance analysis, a component for dialog management, and a component for interaction management. The system for information retrieval and question-answering is tightly integrated within it. The user interface can be phone-based or web-based for written interaction. quaero-catalogue-210x210-v1.6-page-per-page.indd 27 02/10/2013 09:56:33
  • 30. Acoustic Speaker Diarization - LIMSI - CNRS p30 Automatic Speech Transcription - Vocapia p36 Speech-to-Text - Karlsruhe Institute of Technology (KIT) p42 Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 28 quaero-catalogue-210x210-v1.6-page-per-page.indd 28 02/10/2013 09:56:45
  • 31. MediaSpeech®alignement -Vecsys p32 Corinat®- Vecsys p38 Automatic Speech Recognition - RWTH Aachen University p34 Language Identification - Vocapia p40 29 quaero-catalogue-210x210-v1.6-page-per-page.indd 29 02/10/2013 09:56:45
  • 32. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 30 The module aims at performing automatic segmentation and clustering of an input audio according to speaker identity using acoustic cues. Target users and customers Multimedia document indexing and archiving services. Partners: LIMSI-CNRS Application sectors Multimedia document management• Search by content into audio-visual documents• quaero-catalogue-210x210-v1.6-page-per-page.indd 30 02/10/2013 09:56:59
  • 33. 31 Acoustic Speaker Diarization Contact details: Claude Barras claude.barras@limsi.fr Description: Technical requirements: Conditions for access and use: LIMSI-CNRS Spoken Language Processing Group B.P. 133 91403 Orsay Cedex France http://www.limsi.fr/tlp/ A standard PC with Linux operating system. The technology developed at LIMSI-CNRS is available for licensing on a case-by-case basis. Speaker diarization is the process of partitioning an input audio stream into homogeneous segments according to their speaker identity. This partitioning is a useful preprocessing step for an automatic speech transcription system, but it can also improve the readability of the transcription by structuring the audio stream into speaker turns. One of the major issues is that the number of speakers in the audio stream is generally unknown a priori and needs to be automatically determined. Given samples of known speaker’s voices, speaker verification techniques can be further applied and provide clusters of identified speaker. The LIMSI multi-stage speaker diarization system combines an agglomerative clustering based on Bayesian information criterion (BIC) with a second clustering stage using speaker identification (SID) techniques with more complex models. This system participated to several evaluations on acoustic speaker diarization, on US English Broadcast News for NIST Rich Transcription 2004 Fall (NIST RT’04F) and on French broadcast radio and TV news and conversations for the ESTER-1 and ESTER-2 evaluation campaigns, providing state-of-the-art performances. Within the QUAERO program, LIMSI is developing improved speaker diarization and speaker tracking systems for broadcast news but also for more interactive data like talk shows. It is a building block of the system presented by QUAERO partners to the REPERE challenge on multimodal person identification. quaero-catalogue-210x210-v1.6-page-per-page.indd 31 02/10/2013 09:57:00
  • 34. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 32 Audio and Text synchronization tool Target users and customers E-editors• Media content producers• Media application developers• Search interface integrators• Partners: Vecsys Bertin Technologies Exalead Application sectors Public/Private debates and conference: E.g.• Parliament, Meetings E-learning/E-books: E.g. Audiobook• Media Asset Management: E.g. Search in• annotated media streams (TV, radio, films…) quaero-catalogue-210x210-v1.6-page-per-page.indd 32 02/10/2013 09:57:14
  • 35. 33 MediaSpeech® alignment Contact details: Ariane Nabeth-Halber anabeth@vecsys.fr Technical requirements: Conditions for access and use: Vecsys Parc d’Activité du Pas du Lac 10 bis avenue André Marie Ampère 78180 Montigny-le-Bretonneux France http://www.vecsys.fr Standard Web access Available in SaaS mode or installed on a server or installed as Virtual Machine in MediaSpeech® product line. Quotation on request Description: This technology synchronizes an audio stream with its associated text transcript: it takes as inputs both audio stream and raw transcript and produces as output a “time coded” transcript, i.e. each word or group of words is associated with its precise occurrence in the audio stream. The technology is pretty robust and handles nicely slight variations between audio speech and text transcript. quaero-catalogue-210x210-v1.6-page-per-page.indd 33 02/10/2013 09:57:15
  • 36. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 34 Automatic speech recognition, also known as speech-to-text, is the transcription of speech into (machine- readable) text by a computer Target users and customers Researchers• Developers• Integrators• Partners: RWTH Aachen University Application sectors The use of automatic speech recognition is so manifold that it is hard to list here. Main usages today are customer interaction via the telephone, healthcare dictation and usage on car navigation systems and smartphones. With increasingly better technology, these applications are extending to audio mining, speech translation and an increased use of human computer interaction via speech. quaero-catalogue-210x210-v1.6-page-per-page.indd 34 02/10/2013 09:57:29
  • 37. 35 Automatic Speech Recognition Contact details: Volker Steinbiss steinbiss@informatik.rwth-aachen.de Description: Technical requirements: Conditions for access and use: RWTH Aachen University Lehrstuhl Informatik 6 Templergraben 55 52072 Aachen Germany http://www-i6.informatik.rwth-aachen.de Speech translation is a computationally and memory-intensive process, so the typical set-up is to have one or several computers in the internet serving the speech translation requirements of many users. RWTH provides on open-source speech recognizer free of charge for academic usage. Other usage should be subject to a bilateral agreement. Automatic speech recognition is a very hard problem in computer science but more mature than machine translation. After a media hype at the end of the 1990’s, the technology has continuously improved and it has been adopted by the market, e.g. in large deployments in the customer contact sector, in the automation in radiology dictation, or in voice enabled navigation systems in the automotive sector. Public awareness has increased through the use on smart-phones, in particular Siri. The research community concentrates on problems such as the recognition of spontaneous speech or the easy acquisition of new languages. quaero-catalogue-210x210-v1.6-page-per-page.indd 35 02/10/2013 09:57:34
  • 38. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 36 Vocapia Research develops core multilingual large vocabulary speech recognition technologies* for voice interfaces and automatic audio indexing applications. This speech-to-text technology is available for multiple languages. (* Under license from LIMSI-CNRS) Target users and customers The targeted users and customers of speech-to-text transcription technologies are actors in the multimedia and call center sector, including academic and industrial organizations interested in the automatic mining processing of audio or audiovisual documents. Partners: Vocapia Application sectors This core technology can serve as the basis for a variety of applications: multilingual audio indexing, teleconference transcription, telephone speech analytics, transcription of speeches, subtitling… Large vocabulary continuous speech recognition is the key technology for enabling content-based information access in audio and audiovisual documents. Most of the linguistic information is encoded in the audio channel of audiovisual data, which once transcribed can be accessed using text-based tools. Via speech recognition, spoken document retrieval can support random access using specific criteria to relevant portions of audio documents, reducing the time needed to identify recordings in large multimedia databases. Some applications are data-mining, news-on- demand, and media monitoring. quaero-catalogue-210x210-v1.6-page-per-page.indd 36 02/10/2013 09:57:48
  • 39. 37 Automatic Speech Transcription Contact details: Bernard Prouts prouts@vocapia.com contact@vocapia.com +33 (0)1 84 17 01 14 Description: Technical requirements: Conditions for access and use: Vocapia Research 28, rue Jean Rostand Parc Orsay Université 91400 Orsay France www.vocapia.com PC with Linux platform (via licensing use). The VoxSigma software is available both via licensing and via our web service. The Vocapia Research speech transcription system transcribes the speech segments located in an audio file. Currently systems for 17 languages varieties are available for broadcast and web data. Conversational speech transcription systems are available for 7 languages. The transcription system has two main components: an audio partitioner and a word recognizer. The audio partitioner divides the acoustic signal into homogeneous segments, and associates appropriate (document internal) speaker labels with the segments. For each speech segment, the word recognizer determines the sequence of words, associating start and end times and a confidence measure for each word. quaero-catalogue-210x210-v1.6-page-per-page.indd 37 02/10/2013 09:57:49
  • 40. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 38 Language Resources production infrastructure Target users and customers Linguistic resources providers• Audio content transcribers; Media transcribers• Speech processing users and developers• Partners: Vecsys LIMSI-CNRS Application sectors Language resources production• Speech technology industry• Media subtitling, conferences and meetings• transcription services quaero-catalogue-210x210-v1.6-page-per-page.indd 38 02/10/2013 09:58:03
  • 41. 39 Corinat® Contact details: Ariane Nabeth-Halber anabeth@vecsys.fr Description: Technical requirements: Conditions for access and use: Vecsys Parc d’Activité du Pas du Lac 10 bis avenue André Marie Ampère 78180 Montigny-le-Bretonneux France http://www.vecsys.fr Standard Web access Quotation on request Corinat® is a hardware/software infrastructure for language resources production that offers the following functionalities: Data collection (broadcast, conversational)• Audio data automatic pre-processing• Annotation tasks distribution• Annotations semi-automatic post-processing• Corinat® is a high availability platform (24/7), with a web-based interface for language resources production management in any location. quaero-catalogue-210x210-v1.6-page-per-page.indd 39 02/10/2013 09:58:07
  • 42. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 40 Vocapia Research provides a language identification technology* that can identify languages in audio data. (* Under license from LIMSI-CNRS) Target users and customers The targeted users and customers of language recognition technologies are actors in the multimedia and call center sectors, including academic and industrial organizations, as well as actors in the defense domain, interested in the processing of audio documents, and in particular if the collection of documents contains multiple languages. Partners: Vocapia Application sectors A language identification system can be run prior to a speech recognizer. Its output is used to load the appropriate language dependent speech recognition models for the audio document. Alternatively, the language identification might be used to dispatch audio documents or telephone calls to a human operators fluent in the corresponding identified language. Other potential applications also involve the use of LID as a front-end to a multi-lingual translation system. This technology can also be part of automatic system for spoken data retrieval or automatic enriched transcriptions. quaero-catalogue-210x210-v1.6-page-per-page.indd 40 02/10/2013 09:58:21
  • 43. 41 Language Identification Contact details: Bernard Prouts prouts@vocapia.com contact@vocapia.com +33 (0)1 84 17 01 14 Description: Technical requirements: Conditions for access and use: Vocapia Research 28, rue Jean Rostand Parc Orsay Université 91400 Orsay www.vocapia.com PC with Linux platform (via licensing use). The VoxSigma software is available both via licensing and via our web service. The VoxSigma software suite can recognize the language spoken in an audio document or in speech segments defined in an input XML file. The default set of possible languages and their associated models can be specified by the user. LID systems are available for broadcast and conversational data. Currently 15 languages for broadcast news audio and 50 languages for conversational telephone speech are included in the respective VR LID system. New languages can easily be added to the system. The VoxSigma software suite uses multiple phone-based decoders in parallel to take a decision about which language is in the audio file. The system specifies the language of the audio document along with a confidence score. In the current version, it is assumed that a channel of an audio document is in a single language. In future versions, it is planned to allow multiple languages in a single document. quaero-catalogue-210x210-v1.6-page-per-page.indd 41 02/10/2013 09:58:21
  • 44. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 42 Transcription of human speech into written word sequences Target users and customers Companies who want to integrate the transcription of human speech into their products. Partners: Karlsruhe Institute of Technology (KIT) Application sectors Speech-to-Text technology is key to indexing multimedia content as it is found in multimedia databases or in video and audio collections on the World Wide Web, and to make it searchable by human queries. In addition, it offers a natural interface for submitting and executing queries. This technology is further part of speech-translation services. In communication with machine translation technology, it is possible to design machines that take human speech as input and translate it into a new language. This can be used to enable human-to- human combination across the language barrier or to access languages in a cross-lingual way. quaero-catalogue-210x210-v1.6-page-per-page.indd 42 02/10/2013 09:58:35
  • 45. 43 Speech-to-Text Contact details: Prof. Alex Waibel waibel@ira.uka.de Description: Technical requirements: Conditions for access and use: Karlsruhe Institute of Technology (KIT) Adenauerring 2 76131 Karlsruhe Germany http://isl.anthropomatik.kit.edu Linux based server with 2GB of RAM. Available for licensing on a case-by-case basis. The KIT speech transcription system is based on the JANUS Recognition Toolkit (JRTk) which features the IBIS single pass decoder. The JRTk is a flexible toolkit which follows an object-oriented approach and which is controlled via Tcl/Tk scripting. Recognition can be performed in different modes: In offline mode, the audio to be recognized is first segmented into sentence-like units. Theses segments are then clustered in an unsupervised way according to speaker. Recognition can then be performed in several passes. In between passes, the models are adapted in an unsupervised manner in order to improve the recognition performance. System combination using confusion network combination can be used in addition to further improve recognition performance. In run-on mode, the audio to be recognized is continuously processed without prior segmentation. The output is a steady stream of words. The recognizer can be flexibly configured to meet given real-time requirements, between the poles of recognition accuracy and recognition speed. Within the Quaero project, we are targeting the languages English, French, German, Russian, and Spanish. Given sufficient amounts of training material, the HMM based acoustic models can be easily adapted to additional languages and domains. quaero-catalogue-210x210-v1.6-page-per-page.indd 43 02/10/2013 09:58:41
  • 46. Machine Translation - RWTH Aachen University p46 Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 44 quaero-catalogue-210x210-v1.6-page-per-page.indd 44 02/10/2013 09:58:54
  • 47. Speech Translation - RWTH Aachen University p48 45 quaero-catalogue-210x210-v1.6-page-per-page.indd 45 02/10/2013 09:58:54
  • 48. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 46 Automatic translation of text breaks the language barrier: It allows instant access to information in foreign languages. Target users and customers Researchers• Developers• Integrators• Partners: RWTH Aachen University Application sectors As translation quality is far below the work of professional human translators, machine translation is targeted to situations where instant access and low cost are key and high quality is not demanded, for example: Internet search (cross-language document• retrieval) Internet (on-the-fly translation of foreign-• language websites or news feeds) quaero-catalogue-210x210-v1.6-page-per-page.indd 46 02/10/2013 09:59:06
  • 49. 47 Machine Translation Contact details: Volker Steinbiss steinbiss@informatik.rwth-aachen.de Description: Technical requirements: Conditions for access and use: RWTH Aachen University Lehrstuhl Informatik 6 Templergraben 55 52072 Aachen Germany http://www-i6.informatik.rwth-aachen.de Translation is a memory-intense process, so the typical set-up is to have one or several computers in the internet serving the translation requirements of many users. RWTH provides open-source translation tools free of charge for academic usage. Other usage should be subject to a bilateral agreement. Machine translation is a very hard problem in computer science and has been worked on for decades. The corpus-based methods that emerged in the 1990’s allow the computer to actually learn translation from existing bilingual texts – you could say, from many translation examples. A correct mapping is indeed not easy to learn, as the translation of a word depends on its context, and word orders typically differ across languages. It is fascinating to see this technology improving over the years. The learning methods are more of a mathematical kind and can be applied to any language pair. quaero-catalogue-210x210-v1.6-page-per-page.indd 47 02/10/2013 09:59:12
  • 50. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 48 Automatic translation of speech practically sub-titles – in your native language! – the speech of foreign-language speakers. Target users and customers Researchers• Developers• Integrators• Partners: RWTH Aachen University Application sectors Sub-titling of broadcast via television or internet• Internet search in audio and video material• (cross-language retrieval) quaero-catalogue-210x210-v1.6-page-per-page.indd 48 02/10/2013 09:59:25
  • 51. 49 Speech Translation Contact details: Volker Steinbiss steinbiss@informatik.rwth-aachen.de Description: Technical requirements: Conditions for access and use: RWTH Aachen University Lehrstuhl Informatik 6 Templergraben 55 52072 Aachen Germany http://www-i6.informatik.rwth-aachen.de Speech translation is a computationally and memory-intensive process, so the typical set-up is to have one or several computers in the internet serving the speech translation requirements of many users. RWTH provides on open-source speech recognizer and various open-source tools free of charge for academic usage. Other usage should be subject to a bilateral agreement. In a nutshell, speech translation is the combination of two hard computer science problems, namely speech recognition (automatic transcription of speech into text) and machine translation (automatic translation of a text from a source to a target language). While both technologies do not work perfectly, it is impressive to see them working in combination, in particular when we have not even rudimentary knowledge of the source language – for many of us, this is the case for the Chinese or the Arabic language. The mathematical methods behind both speech recognition and machine translation are related, and the systems draw their knowledge from large amounts of example data. quaero-catalogue-210x210-v1.6-page-per-page.indd 49 02/10/2013 09:59:30
  • 52. Sync Audio Watermarking -Technicolor p52 Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 50 quaero-catalogue-210x210-v1.6-page-per-page.indd 50 02/10/2013 09:59:43
  • 53. SAMuSA: Speech And Music Segmenter and Annotator -Inria p54 Yaafe: Audio feature extractor - Télécom ParisTech p56 51 quaero-catalogue-210x210-v1.6-page-per-page.indd 51 02/10/2013 09:59:43
  • 54. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 52 Technicolor Sync Audio Watermarking technologies Target users and customers Content Owners• Studios• Broadcasters• Content distributors• Partners: Technicolor Application sectors Technicolor Sync Audio Watermarking allows studios and content owners to create more valuable and attractive content by• delivering premium quality information to generate additional earnings through targeted ads,• e-commerce and product placement alongside main screen content Technicolor Sync Audio Watermarking allows broadcasters and content distributors to provide distinctive content and retain audiences• to control complementary content on the 2nd screen• within their branded environment to leverage real-time, qualified behavior metadata to• better understand customers and deliver personalized content and recommendations ContentArmor™ Audio Watermarking allows content owners to deter content leakage by tracking the source of pirated copies. quaero-catalogue-210x210-v1.6-page-per-page.indd 52 02/10/2013 09:59:55
  • 55. 53 Sync Audio Watermarking Contact details: Gwenaël Doërr gwenael.doerr@technicolor.com Description: Technical requirements: Conditions for access and use: Technicolor R&D France 975, avenue des Champs Blancs ZAC des Champs Blancs / CS 176 16 35 576 Cesson-Sévigné France http://www.technicolor.com Technicolor Sync Audio Watermarking detector• works on Android and iOS. The watermark embedder of both technologies• works on Linux and MacOS. Both systems can be licensed as software executables or libraries. With Technicolor Sync Audio Watermarking technologies, studios, content owners, aggregators and distributors can sync live, recorded or time- shifted content and collect qualified metadata. And thanks to Technicolor’s expertise in both watermarking and entertainment services, these solutions are easily integrated into your existing post- production, broadcast and any new media delivery workflows. Technicolor sync technologies open access to all the benefits of new attractive companion app markets with no additional infrastructure cost. Content identification and a time stamp are inaudibly inserted into the audio signal in post-production or during broadcast. The 2nd screen device picks up the audio signal, decodes the watermark and synchronizes the app on the 2nd screen thanks to the embedded content identification data. Audio watermarking uses the original content audio signal as its transmission channel, ensuring compatibility with all existing TVs, PVRs or DVD/Blu- ray players as well as legacy devices without network interfaces. It works for realtime, time-shifted and recorded content. quaero-catalogue-210x210-v1.6-page-per-page.indd 53 02/10/2013 09:59:56
  • 56. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 54 Speech And Music Segmenter and Annotator Target users and customers The targeted users and customers are the multimedia industry actors, and all academic or industrial laboratories interested in audio document processing. Partners: Inra Application sectors Audio and multimedia document processing• quaero-catalogue-210x210-v1.6-page-per-page.indd 54 02/10/2013 10:00:09
  • 57. 55 SAMuSA: Speech And Music Segmenter and Annotator Contact details: General issues: Patrick Gros patrick.gros@irisa.fr Technical requirements: Conditions for access and use: IRISA/Texmex team Campus de Beaulieu 35042 Rennes Cedex France http://www.irisa.fr/ PC with Unix/Linux OS• SAMuSA is a software that has been developed at Irisa in Rennes and is the property of CNRS and Inria. SAMuSA is currently available as a prototype only. It can be released and supplied under license on a case- by-case basis. Technical issues: Sébastien Campion scampion@irisa.fr Description: As shown on Figure below, the SAMuSA module takes an audio file or stream as an input, and returns a text file containing detected segments of: speech, music and silence. To perform segmentation, SAMuSA uses audio class models as external resources. It also calls external tools for audio feature extraction (Spro software [1]), and for audio segmentation and classification (Audioseg software [2]). These tools are included in the SAMuSA package. Trained on hours of various TV and radio programs, this module provides efficient results: 95% of speech and 90% of music are correctly detected. One hour of audio can be computed in approximately one minute on standard computers. [1] http://gforge.inria.fr/projects/spro/ [2] http://gforge.inria.fr/projects/audioseg/ SAMuSA was developed in Irisa/INRIA Rennes by the Metiss team. The SAMuSA authors are: Frédéric Bimbot, Guillaume Gravier, Olivier Le Blouch. The Spro author is: Guillaume Gravier The Audioseg authors are: Mathieu Ben, Michaël Betser, Guillaume Gravier quaero-catalogue-210x210-v1.6-page-per-page.indd 55 02/10/2013 10:00:13
  • 58. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 56 Yaafe is a low-level and mid-level audio features extractor, designed to extract large number of features over large audio files. Target users and customers Targeted integrators and users are industrial or academic laboratories in the field of audio signal processing and in particular for music information retrieval tasks. Partners: Télécom ParisTech Application sectors Music information retrieval.• Audio segmentation.• quaero-catalogue-210x210-v1.6-page-per-page.indd 56 02/10/2013 10:00:25
  • 59. 57 Yaafe: Audio feature extractor Contact details: S. Essid slim.essid@telecom-paristech.fr Description: Technical requirements: Conditions for access and use: Télécom ParisTech 37 rue Dareau 75014 Paris / France http://www.tsi.telecomparistech.fr/aao/en/2010/02/19/ yaafe-audio-feature-extractor/ Yaafe is a C++/Python software available for linux and Mac. Yaafe has been released under LGPL licence and is available for download on Sourceforge. Some mid-level feature ARE available in a separate library, with a proprietary licence. Yaafe is designed to extract a large number of features simultaneously, in an efficient way. It automatically optimizes features’ computation, so that each intermediate representation (spectrum, CQT, envelope, etc…) is computed only once. Yaafe works in a streaming mode, so it has a low memory footprint and can process arbitrarily long audio files. Available features are spectral features, perceptual features (loudness), MFCC, CQT, chroma, chords, onsets detection. A user can select his own set of features and transformations (derivative, temporal integration), and easily adapt all parameters to his own task. quaero-catalogue-210x210-v1.6-page-per-page.indd 57 02/10/2013 10:00:32
  • 60. Colorimetric Correction System - Jouve p60 Document Reader -A2iA p66 Handwriting Recognition System - Jouve p72 Recognition of Handwritten Text - RWTH Aachen University p78 Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 58 quaero-catalogue-210x210-v1.6-page-per-page.indd 58 02/10/2013 10:00:44
  • 61. Document Classification System - Jouve p62 Document Structuring System - Jouve p68 Image Descreening System - Jouve p74 Document Layout Analysis System - Jouve p64 Grey Level Character Recognition System -Jouve p70 Image Resizing for Print on Demand Scanning - Jouve p76 59 quaero-catalogue-210x210-v1.6-page-per-page.indd 59 02/10/2013 10:00:44
  • 62. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 60 A specific tool to create a suitable colorimetric correction and check its stability over time Target users and customers Everyone who has to deal with highcolorimetric constraints. Partners: Jouve Application sectors Patrimony• Industry• quaero-catalogue-210x210-v1.6-page-per-page.indd 60 02/10/2013 10:00:58
  • 63. 61 Colorimetric Correction System Contact details: Jean-Pierre Raysz jpraysz@jouve.fr Technical requirements: Conditions for access and use: Jouve R&D 1, rue du Dr Sauvé 53000 Mayenne France www.jouve.com Any Posix compliant system Ask Jouve Description: The system uses a file containing reference values of calibration target and the image obtained from target scanning. A profile is created from this file. In order to improve correction, a table of colors transformation is integrated to the system. To guarantee the required quality, the system checks several times the values of a calibration target against the specifications. quaero-catalogue-210x210-v1.6-page-per-page.indd 61 02/10/2013 10:01:04
  • 64. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 62 A generic tool for classifying documents based on a hybrid learning technique Target users and customers Everyone who has to deal with document classification with a large amount of already classified documents. Partners: Jouve Application sectors Industrial property• Scientific Edition• quaero-catalogue-210x210-v1.6-page-per-page.indd 62 02/10/2013 10:01:17
  • 65. 63 Document Classification System Contact details: Gustavo Crispino gcrispino@jouve.fr Technical requirements: Conditions for access and use: Jouve R&D 30, rue du Gard 62300 Lens France www.jouve.com Any Posix compliant system Ask Jouve Description: The 100% automatic system is based on linguistic resources that are extracted from already classified documents. On a 100 classes patent preclassification task, this system achieves 85% precision (that is 5% better than human operators for this task). quaero-catalogue-210x210-v1.6-page-per-page.indd 63 02/10/2013 10:01:22
  • 66. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 64 A generic tool to identify and extract regions of text by analyzing connected components Target users and customers Everyone who has to deal with document image analysis. Layout analysis is the first major step in a document image analysis workflow. The correctness of the output of page segmentation and region classification is crucial as the resulting representation is the basis for all subsequent analysis and recognition processes. Partners: Jouve Application sectors Industry• Service• Patrimony• Edition• Administration• quaero-catalogue-210x210-v1.6-page-per-page.indd 64 02/10/2013 10:01:35
  • 67. 65 Document Layout Analysis System Contact details: Jean-Pierre Raysz jpraysz@jouve.fr Technical requirements: Conditions for access and use: Jouve R&D 1, rue du Dr Sauvé 53000 Mayenne France www.jouve.com Any Posix compliant system Ask Jouve Description: The system identifies and extracts regions of text by analyzing connected components constrained by black and white (background) separators. The rest is filtered out as non-text. First, the image is binarized, any skew is corrected and black page borders are removed. Subsequently, connected components are extracted and filtered according to their size (very small components are filtered out). quaero-catalogue-210x210-v1.6-page-per-page.indd 65 02/10/2013 10:01:43
  • 68. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 66 Classification of all types of paper documents, Data Extraction and Mail Processing and Workflow Automation Target users and customers Independent Software Vendors• Business Process Outsourcers• Partners: A2iA Application sectors Bank, Insurance, Administration, Telecom and Utility Companies, Historical Document Conversion quaero-catalogue-210x210-v1.6-page-per-page.indd 66 02/10/2013 10:01:55
  • 69. 67 Document Reader Contact details: Venceslas Cartier venceslas.cartier@a2ia.com Technical requirements: Conditions for access and use: A2iA 39, rue de la Bienfaisance 75008 Paris France www.a2ia.com Wintel Platform Upon request Description: Classification of all types of paper documents A2iA DocumentReader classifies digitized documents into user-defined classes or “categories” (letters, contracts, claim forms, accounts receivable, etc.) based on both their geometry and their content. The software analyzes the layout of items on the document. Then, using a general dictionary and trade vocabulary, it carries out a literal transcription of the handwritten and/or typed areas. A2iA DocumentReader can then extract key-words or phrases in order to determine the category of the document. Data Extraction A2iA DocumentReader uses 3 methods to extract data from all types of paper documents: Extraction from predefined documents. Some documents (such as checks, bank documents and envelopes) are preconfigured within A2iA DocumentReader. The software recognizes their structure, the format of data to be extracted and their location on the document.Extraction from structured documents. A2iA DocumentReader recognizes and extracts data within a fixed location on the document.Extraction from semi-structured documents. The layout of the document varies but the data to be extracted remains unchanged. A2iA DocumentReader locates this data by its format and the proximity of key-words, wherever they appear on the document. Mail Processing and Workflow Automation A2iA DocumentReader analyzes the entire envelope or folder on a wholistic level, just as a human would, to identify its purpose and subject-matter (termination of subscription, request for assistance, change of address, etc.). All of the documents together can have a different meaning or purpose than a single document on its own. A2iA DocumentReader then transmits the digital data to the classification application in order to route the mail to the correct person or department. Mail is sent to the appropriate location as soon as it arrives: processing and response times are minimized, workflow automated, and manual labor decreased. quaero-catalogue-210x210-v1.6-page-per-page.indd 67 02/10/2013 10:01:56
  • 70. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 68 A generic tool to recognize the logical structure of documents from a OCR stream Target users and customers Everyone who has to deal with electronic document encoding of from the original source material and needs to consider the hierarchical structure represented in the digitized document. Partners: Jouve Application sectors Industry• Service• Patrimony• Administration• quaero-catalogue-210x210-v1.6-page-per-page.indd 68 02/10/2013 10:02:10
  • 71. 69 Document Structuring System Contact details: Jean-Pierre Raysz jpraysz@jouve.fr Technical requirements: Conditions for access and use: Jouve R&D 1, rue du Dr Sauvé 53000 Mayenne France www.jouve.com Any Posix compliant system Ask Jouve Description: The system recognizes the logical structure of documents from a OCR stream in accordance with the descriptions of a model (DTD, XML Schema). The result is a hierarchically structured flow. The model involves both knowledge of the macro-structure of the documents and the micro-structure of their content. quaero-catalogue-210x210-v1.6-page-per-page.indd 69 02/10/2013 10:02:16
  • 72. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 70 A recognition engine for degraded printed documents Target users and customers Everyone who has to deal with Character recognition on grey level images. Specifically targeted for low quality documents, the system also outperforms on the shelf OCR engines for good quality images. Partners: Jouve Application sectors Heritage scanning• Printing• quaero-catalogue-210x210-v1.6-page-per-page.indd 70 02/10/2013 10:02:29
  • 73. 71 Grey Level Character Recognition System Contact details: Jean-Pierre Raysz jpraysz@jouve.fr Description: Technical requirements: Conditions for access and use: Jouve R&D 1, rue du Dr Sauvé 53000 Mayenne France www.jouve.com Any Posix compliant system Ask Jouve Despite all other OCR engines, this system processes grey level images directly (without using a temporary B&W image). Using all the information present in the image, this system is able to recognize degraded characters. quaero-catalogue-210x210-v1.6-page-per-page.indd 71 02/10/2013 10:02:32
  • 74. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 72 Capture handwritten and machine- printed data from documents Target users and customers Everyone who has to deal with forms containing handwritten fields or to process incoming mails Partners: Jouve Application sectors Banking• Healthcare• Government• Administration• quaero-catalogue-210x210-v1.6-page-per-page.indd 72 02/10/2013 10:02:44
  • 75. 73 Handwriting Recognition System Contact details: Jean-Pierre Raysz jpraysz@jouve.fr Technical requirements: Conditions for access and use: Jouve R&D 1, rue du Dr Sauvé 53000 Mayenne France www.jouve.com Any Posix compliant system Ask Jouve Description: JOUVE ICR (Intelligent Character Recognition) engine is a combination of two complementary systems: HMM and multidimensional recurrent neural networks. This engine has the advantage of dealing with input data of varying size and taking the context into account. JOUVE ICR carries on increasing recognition rate of handwritten fields in forms, using links between the fields. quaero-catalogue-210x210-v1.6-page-per-page.indd 73 02/10/2013 10:02:50
  • 76. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 74 A system that removes annoying halftones in scanned images Target users and customers Everyone who has to deal with high quality reproduction of halftone images. Partners: Jouve Application sectors Heritage scanning• Printing• quaero-catalogue-210x210-v1.6-page-per-page.indd 74 02/10/2013 10:03:02
  • 77. 75 Image Descreening System Contact details: Christophe Lebouleux clebouleux@jouve.fr Technical requirements: Conditions for access and use: Jouve R&D 1, rue du Dr Sauvé 53000 Mayenne France www.jouve.com Any Posix compliant system Ask Jouve Description: Halftone is a process to reproduce photographs or other images in which the various tones of grey or color are produced by variously sized dots of ink. When a document using this process is scanned, a very uncomfortable screening effect may appear. The system uses a combination of removal of peaks in Fourier image and local Gaussian blur. quaero-catalogue-210x210-v1.6-page-per-page.indd 75 02/10/2013 10:03:10
  • 78. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 76 A specific tool for recreating matter that was lost during the scanning process of bonded books. Target users and customers Everyone who has to deal with high quality reproduction of bonded books. Partners: Jouve Application sectors Heritage scanning• Printing• quaero-catalogue-210x210-v1.6-page-per-page.indd 76 02/10/2013 10:03:22
  • 79. 77 Image Resizing for Print on Demand Scanning Contact details: Christophe Lebouleux clebouleux@jouve.fr Technical requirements: Conditions for access and use: Jouve R&D 1, rue du Dr Sauvé 53000 Mayenne France www.jouve.com Any Posix compliant system• Grey level or color images• Ask Jouve Description: In many cases, when documents have been debinded before scanning (that suppresses a part of the original), we are asked to provide an image at the original size, and sometimes to provide larger images than the original for reprint purpose. Using Seam Carving technique, we are able to obtain very realistic results. quaero-catalogue-210x210-v1.6-page-per-page.indd 77 02/10/2013 10:03:30
  • 80. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 78 Recognition of handwritten text transforms handwritten text into machine- readable text on a computer. Target users and customers Researchers• Developers• Integrators• Partners: RWTH Aachen University Application sectors Recognition of printed or handwritten text is heavily used in the mass processing of paper mail, filled-out forms and letters e.g. to insurance companies, and has been covered by the media in connection with the mass digitization of books. New usage patterns will evolve from the better coverage of handwriting and difficult font systems like Arabic or Chinese and from the recognition of text in any form of image data that due to digital cameras and the Internet, is being produced and distributed in ever increasing volumes. quaero-catalogue-210x210-v1.6-page-per-page.indd 78 02/10/2013 10:03:42
  • 81. 79 Recognition of Handwritten Text Contact details: Volker Steinbiss steinbiss@informatik.rwth-aachen.de Technical requirements: Conditions for access and use: RWTH Aachen University Lehrstuhl Informatik 6 Templergraben 55 52072 Aachen Germany http://www-i6.informatik.rwth-aachen.de The text needs to be available in digitized form, e.g. through a scanner as part of a digital image or video. Processing takes place on a normal computer. RWTH does currently not provide public access to software in this area. Any usage should be subject to a bilateral agreement. Description: Optical character recognition (OCR) works sufficiently well on printed text but is in particular difficult for handwritten material. This is due to the fact that handwritten material contains a far higher variability than printed one. Methods that have been proven successful in other areas such as speech recognition and machine translation are being exploited to tackle this set of OCR problems. quaero-catalogue-210x210-v1.6-page-per-page.indd 79 02/10/2013 10:03:48
  • 82. Image Clusterization System - Jouve p82 Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 80 quaero-catalogue-210x210-v1.6-page-per-page.indd 80 02/10/2013 10:04:00
  • 83. Image Identification System - Jouve p84 LTU Leading Image Recognition Technologies - LTU technologies p86 81 quaero-catalogue-210x210-v1.6-page-per-page.indd 81 02/10/2013 10:04:00
  • 84. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 82 A generic tool to perform automatic clustering of scanned images Target users and customers Everyone who has to group a large set of images in such a way that images in the same group are more similar to each other than to those in other groups, like for instance, in incoming mail processing. Partners: Jouve Application sectors Banking• Insurance• Industry• quaero-catalogue-210x210-v1.6-page-per-page.indd 82 02/10/2013 10:04:13
  • 85. 83 Image Clusterization System Contact details: Jean-Pierre Raysz jpraysz@jouve.fr Technical requirements: Conditions for access and use: Jouve R&D 1, rue du Dr Sauvé 53000 Mayenne France www.jouve.com Any Posix compliant system Ask Jouve Description: Two kinds of methods have been implemented. The first method consists in applying optical character recognition on pages. Distances are computed between images to classify and images contained in a database of labeled images. The second method consists in randomly selecting a pool of images inside a directory. For each image, invariant key points are extracted and characteristic features are computed (SIFT or SURF) to build the clusters. quaero-catalogue-210x210-v1.6-page-per-page.indd 83 02/10/2013 10:04:19
  • 86. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 84 A generic tool to identify automatically documents, photos and text zones in scanned images Target users and customers Everyone who has to deal with document recognition like identity cards, passports, invoices… Partners: Jouve Application sectors Administration• Banking• Insurance• quaero-catalogue-210x210-v1.6-page-per-page.indd 84 02/10/2013 10:04:31
  • 87. 85 Image Identification System Contact details: Jean-Pierre Raysz jpraysz@jouve.fr Technical requirements: Conditions for access and use: Jouve R&D 1, rue du Dr Sauvé 53000 Mayenne France www.jouve.com Any Posix compliant system Ask Jouve Description: The system searches the best match between image signatures and model signatures. It determines whether the same kind of model is present in the image which has to be segmented or not. The segmentation done on the model is reported in the image to be segmented by applying an affine transformation (translation, rotation and homothety). quaero-catalogue-210x210-v1.6-page-per-page.indd 85 02/10/2013 10:04:36
  • 88. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 86 Leading Image Recognition Technologies Target users and customers Brands• Retailers• Social Media Monitoring companies• Research companies• Government agencies• Partners: LTU technologies Application sectors Visual Brand Intelligence: e-reputation, brand• protection Media Monitoring• M-Commerce and E-Commerce: augmented• reality, interactive catalogs, virtual Shop, advanced search functionalities, etc. Visual Asset Management: Images classification,• Images de-duplication, Images filtering, moderation, etc. quaero-catalogue-210x210-v1.6-page-per-page.indd 86 02/10/2013 10:04:48
  • 89. 87 LTU Leading Image Recognition Technologies Contact details: Frédéric Jahard fjahard@ltutech.com Technical requirements: Conditions for access and use: LTU technologies Headquarter: 132 rue de Rivoli 75001 Paris, France +33 1 53 43 01 68 Coming soon Coming soon US office: 232 Madison Ave New York, NY 10016 USA +1 646 434 0273 http://www.ltutech.com Description: Founded in 1999 by researchers at MIT, Oxford and Inria, LTU provides cutting-edge image recognition technologies and services to global companies and organizations such as Adidas, Kantar Media and Ipsos. LTU’s solutions are available on-demand with LTU Cloud or on an on-premise basis with LTU Enterprise Software. These patented image recognition solutions enable LTU’s clients to effectively manage their visual assets – internally and externally – and innovate by bringing their end-users truly innovative visual experiences. In an image-centric world, LTU’s expertise runs the image recognition gamut from visual search, visual data management, investigations and media monitoring, to e-commerce, brand intelligence, and mobile applications. quaero-catalogue-210x210-v1.6-page-per-page.indd 87 02/10/2013 10:04:55
  • 90. AudioPrint - IRCAM p90 Ircamchord: Automatic Chord Estimation - IRCAM p96 Music Structure - Inria p102 Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 88 quaero-catalogue-210x210-v1.6-page-per-page.indd 88 02/10/2013 10:05:06
  • 91. Ircamaudiosim: Acoustical Similarity Estimation - IRCAM p92 Ircammusicgenre and Ircammusicmood: Genre and Mood Estimation - IRCAM p98 Ircambeat: Music Tempo, Meter, Beat and Downbeat Estimation - IRCAM p94 Ircamsummary: Music Summary Generation and Music Structure Estimation - IRCAM p100 89 quaero-catalogue-210x210-v1.6-page-per-page.indd 89 02/10/2013 10:05:07
  • 92. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 90 AudioPrint captures the acoustical properties by computing a robust representation of the sound Target users and customers AudioPrint is dedicated to middleware integrators that wish to develop audio fingerprint applications (i.e. systems for live recognition of music on air), as well as synchronization frameworks for second screen applications (a mobile device brings contents directly related to the live TV program). The music recognition application can also be used by digital rights management companies. Partners: IRCAM Application sectors Second screen software providers• Digital right management• Music query software developers• quaero-catalogue-210x210-v1.6-page-per-page.indd 90 02/10/2013 10:05:19
  • 93. 91 AudioPrint Contact details: Frédérick Rousseau Frederick.Rousseau@ircam.fr Technical requirements: Conditions for access and use: IRCAM Sound Analysis /Synthesis 1 Place Igor-Stravinsky 75004 Paris France http://www.ircam.fr AudioPrint is available as a static library for Linux, Mac OS X and iOS platforms. Ircam Licence Description: AudioPrint is an efficient technology for live or offline recognition of musical tracks, within a database of learnt tracks. It captures the acoustical properties of the audio signal by computing a symbolic representation of the sound profile that is robust to common alterations. Moreover, it provides a very precise estimation of the temporal offset within the detected musical track. This offset estimation can be used as a means to synchronize devices. quaero-catalogue-210x210-v1.6-page-per-page.indd 91 02/10/2013 10:05:20
  • 94. Semantic Acquisition & Annotation (5) p8 to 17 Q&A (4) p20 to 27 Translation of Text and Speech (2) p46 to 49 Speech Processing (7) p30 to 43 Document Processing (10) p60 to 79 Audio Processing (3) p52 to 57 Object Recognition & Image Clustering (3) p82 to 87 Music Processing (7) p90 to 103 Indexing, Ranking and Retrieval (1) p106 to 107 Content Analysis (4) p110 to 117 Video Analysis & Structuring (12) p124 to 147 Gesture Recognition (1) p120 to 121 Core Technology Modules 92 Ircamaudiosim estimates the acoustical similarity between two music tracks. It can be used to perform music recommendation based on music content similarity. Target users and customers Ircamaudiosim allows the development of music recommendation based on music content similarity. It can therefore be used for any system (online or offline) requiring music recommendation, such as for the development of a recommendation engine for online music service or offline music collection browsing. Partners: IRCAM Application sectors Online music providers• Online music portals• Music players developers• Music software developers• quaero-catalogue-210x210-v1.6-page-per-page.indd 92 02/10/2013 10:05:32
  • 95. 93 Ircamaudiosim: Acoustical Similarity Estimation Contact details: Frédérick Rousseau Frederick.Rousseau@ircam.fr Technical requirements: Conditions for access and use: IRCAM Sound Analysis /Synthesis 1 Place Igor-Stravinsky 75004 Paris France http://www.ircam.fr Ircamaudiosim is available as software or as a dynamic library for Windows, Mac OS-X and Linux platform. Ircam Licence Description: Ircamaudiosim estimates the acoustical similarity between two audio tracks. For this, each music track of a database is first analyzed in terms of its acoustical content (timbre, rhythm, harmony). An efficient representation of this content is used, that allows a fast comparison between two music tracks. Because of this, the system is scalable to large databases. Given a target music track, the most similar (in terms of acoustical content) items of the database can be found quickly and then be used to provide recommendation to the listener. quaero-catalogue-210x210-v1.6-page-per-page.indd 93 02/10/2013 10:05:33