Word/character count & billing Wordfast Classic

Wordfast's way of counting words is slightly different from Ms-Word's statistics (Tools/Wordcount or Tools/Statistics). For example, in the following text:

L'argent de Louis-Philippe

Ms-Word will find 3 words, while Wordfast will find 5 words (a very similar word count is upheld by most translation tools). On average, Wordfast will find from 5 to 10% more words than Ms-Word, depending on the language. The difference is more striking with French, more modest with other languages. This way of counting is in keeping with most translation syndicates and unions in most countries using alphabetic languages.

Discuss the word count issue with your client before starting working on a project.

On tagged documents, tags are counted as one word (regardless of their number of characters or words) and their number is also reported in the analysis final report. A tag is defined as any contiguous series of characters (spaces included) that have the tw4winInternal style.

Note that (as opposed to word count), tags are not included in the character count, because a tag is counted as one word; tags are included in the word count.

The Wordfast word/character count, as with all CAT tools, is based on what the tool considers to be translatable text. This can depend on the way you set up your tool. For example, the use of the "SegmentAll" command will force Wordfast to consider any text as translatable, including isolated fields, figures, etc. which would otherwise be left out of the translation process.

The Wordfast word/character count includes all headers and footers, footnotes, but not fields. Pay attention to word count when auditing a project, or producing an estimate. Ask yourself the question (if applicable) of whether the document(s) contains bookmarks, and if it does, what the author/client wants to do of them; whether graphics or textboxes should be translated, whether headers and footers should be translated, whether the word count is based on source, or target (translated), text, how to count tags, if any (per piece? per word? per character? at what rate?), etc.

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