An automatic spelling correcting algorithm corrects most of the 50,000 misspellings culled from 25,000,000 words of text from seven scientific and scholarly databases. It uses a similarity key to identify words in a large dictionary that are most similar to a particular misspelling, and'then an error-reversal test to select from these the most plausible correction(s).

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The SPEEDCOP (SPElling Error Detection Correction Project) project recently completed at Chemical Abstracts Service (CAS) extracted over 50,000 misspellings from approximately 25,000,000 words of text from seven scientific and scholarly databases. The misspellings were automatically classified and the error types analyzed. The results, which were consistent over the different databases, showed that the expected incidence of misspelling is 0.2%, that 90-95% of spelling errors have only a single mistake, that substitution is homogeneous while transposition is heterogeneous, that omission is the commonest type of misspelling, and that inadvertent doubling of a letter is the most important cause of insertion errors. The more frequently a letter occurs in the text, the more likely it is to be involved in a spelling error. Most misspellings collected by SPEEDCOP are of the type colloquially referred to as "typos" and approximately 90% are unlikely to be repeated in normal spans of text.

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