Sparse representation over learned dictionary for symbol recognition

Thanh Ha Do, Salvatore Tabbone, Oriol Ramos Terrades

    Research output: Contribution to journalArticleResearchpeer-review

    12 Citations (Scopus)


    © 2016 Elsevier B.V. In this paper we propose an original sparse vector model for symbol retrieval task. More specifically, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols.
    Original languageEnglish
    Pages (from-to)36-47
    JournalSignal Processing
    Publication statusPublished - 1 Aug 2016


    • Interest points
    • Learned dictionary
    • Shape context
    • Sparse representation
    • Symbol recognition


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