Centro Propio de Investigación Pattern Recognition and Human Language Technology

Principales cifras de actividad del último año

investigadores 20
subvenciones 291.078 €
contratación 77.880 €

Principales clientes


Líneas I+D+i

  • Image Analysis. Image Analysis.
    Identification of the objects in an image. Statistical and Syntactic Pattern recognition techniques are used. Applications: OCR and document analysis, medical diagnosis, biometric identification, image and video retrieval, classification of chromosomes, aids for the handicapped, manufacturing quality control, etc. Read more.
  • Multimodal Interaction. Multimodal Interaction.
    Technologies to deal with a recent paradigm shift in the design of Pattern Recognition systems, where the traditional concept of full-automation is being changed to systems in which the decision process is conditioned by human feedback. Problems and applications considered within this area include: Relevance-based (image) information retrieval and Interactive-Predictive processing for Computer Assited Machine Translation, as well as for the Interactive Transcription of speech audio streams and text image Documents..
  • Procesamiento de lenguaje natural. Cross-lingual information retrieval and script retrieval.
    For many languages that use non-Roman based indigenous scripts (e.g., Arabic, Greek and Indic languages) one can often find a large amount of user generated transliterated content on the Web in the Roman script. IR in such space is challenging because queries written in either the native or the Roman scripts need to be matched to the documents written in both the scripts. Moreover, transliterated content features extensive spelling variations. We propose a principled solution to handle the cross-script term matching and spelling variation where the terms across the scripts are modelled jointly in a deep-learning architecture and can be compared in a low-dimensional abstract space.
  • Reconocimiento de texto manuscrito. Handwritten Text Recognition.
    Recognition of handwritten text. Hidden Markov models are employed at the backend of this technology after preprocessing and feature extraction from line images. Applications: extraction of electronically readable text from handwritten documents, such as forms, surveys, historical old documents, etc..
  • Reconocimiento de voz. Automatic Speech Recognition.
    The speech utterances are decoded into strings of words or into strings of semantic units. Finite-state grammars are used as the basis of such systems. These finite-state grammars are learnt automatically from real examples of utterances or text. Applications: telephone exchange services, device control by voice, information queries, etc..
  • Traducción automática. Machine Translation.
    The activities of the Machine Translation group began some years ago with the use of finite-state models for speech-to-speech translation and for text-to-text translation in limited domains. This group has developped a number of translation models with the corresponding learning algorithms and a number of prototypes for speech translation and computer-assisted translation. Currently, the Machine Translation group is devoted to the development of new interactive-predictive techniques for computer-assisted translation, techniques for domain adaptation and for continuous text representations..