Hybrid fuzzy-neural systems in handwritten word recognition

Jung Hsien Chiang, Paul D. Gader

研究成果: Article同行評審

40 引文 斯高帕斯(Scopus)


Two hybrid fuzzy neural systems are developed and applied to handwritten word recognition. The word recognition system requires a module that assigns character class membership values to segments of images of handwritten words. The module must accurately represent ambiguities between character classes and assign low membership values to a wide variety of noncharacter segments resulting from erroneous segmentations. Each hybrid is a cascaded system. The first stage of both is a self-organizing feature map (SOFM). The second stages map distances into membership values. The third stage of one system is a multilayer perceptron (MLP). The third stage of the other is a bank of Choquet fuzzy integrals (FI's). The two systems are compared individually and as a combination to the baseline system. The new systems each perform better than the baseline system. The MLP system slightly outperforms the FI system, but the combination of the two outperforms the individual systems with a small increase in computational cost over the MLP system. Recognition rates of over 92% are achieved with a lexicon set having average size of 100. Experiments were performed on a standard test set from the SUNY/USPS CD-ROM database.

頁(從 - 到)497-510
期刊IEEE Transactions on Fuzzy Systems
出版狀態Published - 1997

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 計算機理論與數學
  • 人工智慧
  • 應用數學


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