Abstract
We propose a new method for off-line recognition of unconstrained handwritten words consisting of Korean and numeric characters. To overcome the difficulty in separating touching characters, we adopt an over-segmentation strategy. Given a slice of the input word image, we find the optimal segment combination using a lexicon-driven word scoring technique and a nearest-neighbor classifier. The optimal combination gives the final segmentation positions for individual characters, along with the best matching word in the lexicon. Superiority of the proposed system has been proven by testing it with 908 images of unconstrained words handwritten on live mail pieces.
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