Application of independent component analysis to handwritten Japanese character recognition

We explore an approach to recognizing Japanese Hiragana characters utilizing independent components of input images (we call this method ICA-matching). These components are extracted by the fast ICA algorithm proposed by Hyvarinen and Oja (1997). We propose several formats of inputs, which are different in how a character image is transformed into time sequences. From recognition experiments, we show that ICA-matching outperforms conventional methods in some cases. However, in order to realize high performance, we focus on the following parameters: dimensions of feature vectors and the rate of noise added to the training data. The question of how these parameters are related to the performance of ICA-matching is discussed