Combining statistical pattern recognition approach with neural networks for recognition of large-set categories
Yoshimasa Kimura, Toru Wakahara, K. Odaka · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
We present a two-stage hierarchical system consisting of a statistical pattern recognition (SPR) module and artificial neural network (ANN) to recognize a large number of categories including similar category sets. In the first stage, the SPR module performs classification. If the first candidate does not belong to a pre-determined similar category set, the first candidate is accepted as the final result; otherwise, the first candidate is sent to the ANN module. In the second stage, ANN performs classification for similar categories to select a correct candidate from the predetermined candidate set designated by the first candidate. The new scheme offers improved system performance by sharing tasks between SPR and ANN according to the degree of classification difficulty and forming specialized ANNs for each similar category. The system achieves higher performance for the recognition of 3,201 handprinted characters than a traditional system constructed with just the SPR module.