As the rise of pen-enabled interfaces is accompanied with an increased number of techniques for recognition of penbased input, recent trends in symbol recognition show an escalation in systems complexity (number of features, classifiers combination) or the over-specialization of systems to specific datasets or applications. In spite of the importance of representation space in feature-based methods, few works focus on the design of feature sets adapted to a large variety of symbols, and no universal representation space was proposed as a benchmarking reference. We introduce in this work HBF49, a unique set of features for the representation of hand-drawn symbols to be used as a reference for evaluation of symbol recognition systems. An empirical constructive approach is adopted for designing this set of 49 simple features, able to handle a large diversity of symbols in various experimental contexts. An original effort is made for guaranteeing transparency of features design and reproducibility of experiments. We demonstrate that using off-the-shelf statistical classifiers, the HBF49 representation performs comparably or better than state-of-the-art results reported on 8 databases of hand-drawn objects. We also obtain a good recognition performance for user-defined gestures that further attests the ability of HBF49 to deal with a great variety of symbols.
Delaye and Anquetil REF presented a set of 49 features, called HBF49, for the representation of hand-drawn symbols. And HBF49 can be used as a reference to evaluate a symbol recognition system.
HBF49 feature set: A first unified baseline for online symbol recognition
Pattern Recognit.
Pattern Recognit.
Computer Science