Production year

Social networks

    Number of documents

    7

    Harold Mouchère


    Associate Professor, University of Nantes


    "Jinpeng Li"   

    Journal articles2 documents

    • Jinpeng Li, Harold Mouchère, Christian Viard-Gaudin. An annotation assistance system using an unsupervised codebook composed of handwritten graphical multi-stroke symbols. Pattern Recognition Letters, Elsevier, 2014, 35 (1), pp. 46-57. ⟨10.1016/j.patrec.2012.11.018⟩. ⟨hal-00766693⟩
    • Harold Mouchère, Jinpeng Li, Christian Viard-Gaudin, Zhaoxin Chen. A dynamic Time Warping Algorithm for Recognition of Multi-Stroke On-Line Handwriten Characters. Natural Science Edition, Journal of South China University of Technology, 2013, 41 (7), pp. 107-113. ⟨10.3969/j.issn.1000-565X.2013.07.000⟩. ⟨hal-00933679⟩

    Conference papers5 documents

    • Jinpeng Li, Harold Mouchère, Christian Viard-Gaudin. Quantifying spatial relations to discover handwritten graphical symbols. Document Recognition and Retrieval XIX, Part of the IS&T/SPIE 24th Annual Symposium on Electronic Imaging, Jan 2012, San Francisco, United States. 2012. 〈hal-00672002〉
    • Jinpeng Li, Harold Mouchère, Christian Viard-Gaudin. Une distance entre deux ensembles de séquences avec la contrainte de continuité. Colloque International Francophone sur l'Ecrit et le Document (CIFED2012), Mar 2012, Bordeaux, France. 2012. 〈hal-00671998〉
    • Jinpeng Li, Harold Mouchère, Christian Viard-Gaudin. Reducing AnnotationWorkload Using a Codebook Mapping and its Evaluation in On-Line Handwriting. 2012 International Conference on Frontiers in Handwriting Recognition, Sep 2012, Bari, Italy. pp.1-6. ⟨hal-00717851⟩
    • Jinpeng Li, Harold Mouchère, Christian Viard-Gaudin. Symbol Knowledge Extraction from a Simple Graphical Language. 11th International Conference on Document Analysis and Recognition, ICDAR 2011, Sep 2011, Beijing, China. ⟨hal-00615208⟩
    • Jinpeng Li, Harold Mouchère, Christian Viard-Gaudin. UNSUPERVISED HANDWRITTEN GRAPHICAL SYMBOL LEARNING Using Minimum Description Length Principle on Relational Graph. International Conference on Knowledge Discovery and Information Retrieval, KDIR 2011, Oct 2011, Paris, France. ⟨hal-00615217⟩