In segmentation-by-recognition methods, words are recognized by searching for the aggregation of basic segments (strokes) that best match one of the sequences of letters allowed by the lexicon. The presence of spurious strokes (“extra ink”) impairs recognition, since they have to be associated anyway with some letter of the word. This paper describes how we solved this problem by properly defining initial crisp evaluations to filter out unambiguous extra ink strokes, and then, for ambiguous strokes, by defining fuzzy measures that evaluate the plausibility with which they can be associated to a class of extra ink instead of to a letter class, and finally by devising search algorithms that allow extra ink strokes to be “consumed” without associating them to letters: it is the matching algorithm that decides if it is less costly to consider a hypothesis as extra ink or to associate it to some letter.