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In domain of analytic cursive word recognition, there are two main approaches: explicit segmentation based and implicit segmentation based. However, both approaches have their own shortcomings. To overcome individual weaknesses, this paper presents a hybrid strategy for recognition of strings of characters (words or numerals). In a two stage dynamic programming based, lexicon driven approach, first an explicit segmentation is applied to segment either cursive handwritten words or numeric strings. However, at this stage, segmentation points are not finalized. In the second verification stage, statistical features are extracted from each segmented area to recognize characters using a trained neural network. To enhance segmentation and recognition accuracy, lexicon is consulted using existing dynamic programming matching techniques. Accordingly, segmentation points are altered to decide true character boundaries by using lexicon feedback. A rigorous experimental protocol shows high performance of the proposed method for cursive handwritten words and numeral strings.