We describe an automated script identification system for typeset document images. Templates for each script are created by clustering textual symbols from a training set. Symbols from new images are compared to the templates to find the best script. Our current system processes thirteen scripts with minimal preprocessing and high accuracy.

We describe a system that automatically identifies the script used in documents stored electronically in image form. The system can learn to distinguish any number of scripts. It develops a set of representative symbols (templates) for each script by clustering textual symbols from a set of training documents and representing each cluster by its centroid. "Textual symbols" include discrete characters in scripts such as Cyrillic, as well as adjoined characters, character fragments, and whole words in connected scripts such as Arabic. To identify a new document's script, the system compares a subset of symbols from the document to each script's templates, screening out rare or unreliable templates, and choosing the script whose templates provide the best match. Our current system, trained on thirteen scripts, correctly identifies all test documents except those printed in fonts that differ markedly from fonts in the training set.

The authors study the principles governing the power and efficiency of the default hierarchy, a system of knowledge acquisition and representation. The default hierarchy trains automatically, yet yields a set of rules which can be easily assessed and analyzed. Rules are organized in a hierarchical structure containing general (default) and specific rules. In training the hierarchy, general rules are learned before specific rules. In using the hierarchy, specific rules are accessed first, with default rules used when no specific rules apply. The main results concern the properties of the default hierarchy architecture, as revealed by its application to English pronunciation. Evaluating the hierarchy as a pronouncer of English, the authors find that its rules capture several key features of English spelling. The default hierarchy pronounces English better than the neural network NETtalk, and almost as well as expert-devised systems.>