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This paper describes an offline handwriting recognition system for Amharic words based on lexicon. The system computes direction fields of scanned handwritten documents, from which pseudo-characters are segmented. The pseudo-characters are organized based on their proximity and direction to form text lines. Words are then segmented by analyzing the relative gap between subsequent pseudo-characters in text lines. For each segmented word image, the structural characteristics of pseudo-characters are syntactically analyzed to predict a set of plausible characters forming the word. The most likelihood word is finally selected among candidates by matching against the lexicon. The system is tested by a database of unconstrained handwritten Amharic documents collected from various sources. The lexicon is prepared from words appearing in the collected database.