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Truecasing is the process of restoring case information to badly-cased or non-cased text. This paper explores truecasing issues and proposes a statistical, language modeling based truecaser which achieves an accuracy of ~98% on news articles. Task based evaluation shows a 26% F-measure improvement in named entity recognition when using truecasing. In the context of automatic content extraction, mention detection on automatic speech recognition text is also improved by a factor of 8. Truecasing also enhances machine translation output legibility and yields a BLEU score improvement of 80.2%. This paper argues for the use of truecasing as a valuable component in text processing applications.
Lita et al. (2003) studied this question.