We describe a system that automatically identifies the script used tin documents stored electronically in image for. 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.
J. Hochberg, L. Kerns, P. Kelly and T. Thomas. Automatic script identification from images using cluster-based templates. In Proceedings of the Third International Conference on Document Analysis and Recognition (ICDAR '95). IEEE Computer Society Press, pp. 378-381, 1995. Los Alamos National Laboratory Technical Report LA-UR-95-0021. [ Abstract | PDF (32 KB) ]
