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The identification of a person on the basis of scanned images of handwriting is a useful biometric technique with application in forensic document analysis. This study describes the design and implementation of a system that identifies the writer using offline Arabic handwritten text. The key point is using multiple features to capture different aspects of handwriting individuality and to operate at different level of analysis with the aim of improving identification performance. Fuzzy logic (FL) and genetic algorithm (GA) have been used in a complementary fashion to fuse (combine) extracted features as well as to deal with the ambiguity of human judgment of handwritings similarity. GA is used to help construct and tune fuzzy membership functions that are necessary to categorise the strength of existence of handwritings features similarity through FL, with the purpose of yielding high correct identification rates. The final results indicate and clarify that the proposed system achieves an excellent test accuracy of identification rated up to 96% for Arabic text.