Short CV

Biographical information

  • Laurent Heutte (30/5/64) received his PhD degree in Computer Engineering from the University of Rouen, France, in 1994. From 1996 to 2004, he was a Senior Lecturer in Computer Engineering and Control System at the University of Rouen. Since 2004, he has been a Professor in the same university where he is currently the Head of the « Document and Learning » group in LITIS lab. Professor Heutte’s present research interests are statistical pattern recognition and multiple classifier systems applied to off-line cursive handwriting analysis and recognition, handwritten document layout analysis, information extraction from handwritten documents and image classification.

Education – Degrees

  • 2003: Ph.D. Supervision degree (Habilitation à Diriger les Recherches), Faculty of Sciences, University of Rouen
  • 1994: Ph.D. degree in Computer Science, Faculty of Sciences, University of Rouen
  • 1991: M.Sc. degree (DEA/DESS) in Computer Engineering and Control System, Faculty of Sciences, University of Rouen
  • 1990: Computer Engineering and Control Systems, Faculty of Sciences, University of Rouen

Position held

  • 2004-        : Professor, LITIS Lab., Faculty of Sciences, University of Rouen
  • 1996-2004: Senior Lecturer, PSI Lab., Faculty of Sciences, University of Rouen
  • 1994-1996: Assistant Lecturer, La3i Lab., Faculty of Sciences, University of Rouen
  • 1992-1994: Research Associate (Ph.D. student) in the Automatic Reading Group of the french company MATRA CAP Systèmes, Paris and with La3i Lab.
  • 1991-1992: Research Student at La3i Lab., University of Rouen and with the french company MATRA MS2i, Paris

Fields of research interest and experience

  • Pattern recognition, image processing, feature definition
  • Statistical classification, clustering, feature selection and extraction, classifier combination and ensembles
  • Off-line handwriting analysis, writer identification, handwritten character and cursive word recognition
  • Automatic reading of bankchecks, postal addresses and full-page handwritten texts
  • Information extraction from and information retrieval of handwritten documents
  • Handwritten document segmentation, complex handwritten document layout analysis and extraction
  • Algorithmical and software optimization, genetic algorithms, multiple agent systems