In this paper, we propose a method to compose a classifier by non-linear discriminant analysis using kernel method combined with kernel feature selection for holistic recognition of historical hand-written string. Through experiments using historical hand-written string database HCD2, we show that our approach can obtain high recognition accuracy comparable to that of individual character recognition.
['Ryo Inoue', 'Hidehisa Nakayama', 'Nei Kato']
Historical Hand-Written String Recognition by Non-linear Discriminant Analysis using Kernel Feature Selection