In this paper, authors discuss on a lexicon directed algorithm for recognition of unconstrained handwritten words (cursive, discrete, or mixed) such as those encountered in mail pieces. The procedure consists of binarization, presegmentation, intermediate feature extraction, segmentation recognition, and post-processing. The segmentation recognition and the post-processing are repeated for all lexicon words while the binarization to the intermediate feature extraction are applied once for an input word. This algorithm is essentially non hierarchical in character segmentation and recognition which are performed in a single segmentation recognition process. The result of performance evaluation using large handwritten address block database, and algorithm improvements are described and discussed to achieve higher recognition accuracy and speed. Experimental studies with about 3000 word images indicate that overall accuracy in the range of 91% to 98% depending on the size of the lexicon (assumed to contain correct word) are achievable with the processing speed of 20 to 30 word per minute on typical work station.