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In this paper we combine complementary features based on foreground and background information in an HMM-based classifier to recognize handwritten isolated characters and numeral strings. A zoning scheme based on column and row models provides a way of dividing the character into zones without making the features size variant. This strategy allows us to avoid the character normalization, while it provides a way of having information from specific zones of the character. The experimental results on 10 digit classes, 52 character classes and 6 classes of numeral strings of different lengths have shown that the proposed features are highly discriminant.