Handwritten Document Image Analysis at Los Alamos: Script, Language, and Writer Identification (original) (raw)

1999

Abstract

A system for automatically identifying the script used in a handwritten document image is described. The system was developed using a 496-document dataset representing six scripts, eight languages, and 281 writers. Documents were characterized by the mean, standard deviation, and skew of five connected component features. A linear discriminant analysis was used to classify new documents, and tested using writer-sensitive cross-validation. Classification accuracy averaged 88% across the six scripts. The same method, applied within the Roman subcorpus, discriminated English and German documents with 85% accuracy. Pilot results indicate that a variation of the method may be applicable to writer identification. 1. Introduction Script and language identification are important parts of the automatic processing of document images in an international environment. A document's script (e.g., Cyrillic or Roman) must be known in order to choose an appropriate optical character recognition ...

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