Two novel contributions to Content Based Image Retrieval are presented and

discussed. The fi rst is a search engine for font recognition. The intended usage

is the search in very large font databases. The input to the search engine is an

image of a text line, and the output is the name of the font used when printing

the text. After pre-processing and segmentation of the input image, a local

approach is used, where features are calculated for individual characters. The

method is based on eigenimages calculated from edge fi ltered character images,

which enables compact feature vectors that can be computed rapidly. A system

for visualizing the entire font database is also proposed. Applying geometry

preserving linear- and non-linear manifold learning methods, the structure of

the high-dimensional feature space is mapped to a two-dimensional representa-

tion, which can be reorganized into a grid-based display.