Linear discriminant analysis (LDA) is a basic tool of pattern recognition, and it is used in extensive fields, e.g. face identification. However, LDA is poor at adaptability since it is a batch type algorithm. To overcome this, new algorithms of online LDA are proposed in the present paper. In face identification task, it is experimentally shown that the new algorithms are about two times faster than the previously proposed algorithm in terms of the number of required examples, while the previous algorithm attains better final performance than the new algorithms after sufficient steps of learning. The meaning of new algorithms are also discussed theoretically, and they are suggested to be corresponding to combination of PCA and Mahalanobis distance.

In this paper, a novel approach is proposed to identify the detailed typeface of Gothic characters in document images. The identification is performed by evaluating two types of typeface models, named the Gs-pattern and the Gd-pattern according to the principle of MDL. The typeface models are generated from the observed character image by using morphology and are viewed as approximating expressions of the observed character. Consequently, this method is unique in that it is free from both character recognition and dictionary lookup.