Handwritten alphabet and digit character recognition using feature extracting neural network and modified self-organizing map
Kenji Nakayama, Y. Chigawa, Osamu Hasegawa · 2003
A new pattern recognition method is proposed for handwritten alphabet and digit characteristics. The feature point distribution of a standard pattern is mapped onto that of a distorted pattern, through a modified self-organizing map (SOM). The distorted pattern is recognized based on similarity between both feature point distributions. The modified SOM has the following advantages. First, the number of feature points is small, and these are classified into several groups. Second, the mapping is carried out in the variable ring shape region to find a suitable pairing of the feature points. Third, the feature points are selected from both the standard and the distorted patterns to avoid any vibration. Finally, neighborhoods are selected along lines of the patterns. These improvements can provide stable and fast feature point mapping. Computer simulations demonstrated that the proposed method can adapt to a variety of pattern distortions.>