This paper presents a new lexicon reduction method for historical Arabic scripts that compares the input subword image with the lexicon entries, and selects the most similar ones. In comparing two subword images, more importance is given to the prominent shape regions, defined as those local regions of a subword that distinguish it from other lexicon subwords. In this method, first a retrieval-based measure is applied to compute a distinction score for each local region, indicating how prominent that region is. These scores are subsequently used in a proposed distance measure to modulate the weights of corresponding shape features, where most distinctive regions are given more weight. A global shape-based lexicon reduction based on the characteristic loci is used as well, to complement the local subword descriptors. We evaluated the performance of our proposed method on the Ibn Sina database, containing more than 12,000 subwords extracted from a historical Arabic document, and the degree of reduction of 98.15 % with an accuracy of 90.15 % was achieved.