Hybrid fuzzy neural systems for robust handwritten word recognition
Jung-Hsien Chiang, Paul Gader · 1995
The research work described in this dissertation is aimed at developing improved handwritten character classifiers for use in off-line handwritten word recognition. In particular, our goal is to develop algorithms that can accurately reflect character class ambiguities and that can detect non-character inputs. Two specific hybrid models were developed in this study. We designed, implemented, and successfully demonstrated a hybrid neural network model for character confidence assignment using a cascade of a Kohonen self-organizing feature map (SOFM) and a multi-layer feedforward network (MLFN). The other novel hybrid neural network/fuzzy integral model for character confidence assignment use a cascade of the SOFM and a set of Choquet fuzzy integrals (FI). These new methodologies have resulted in significant improvements in handwritten word recognition performance. Recognition rates of over 90% were achieved using a single word recognizer and an average lexicon size of 100.