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<p>We have developed the notion of lexicon density as a metric to measure the expected accuracy of handwritten word recognizers. Thus far, researchers have used the size of the lexicon as a gauge for the difficulty of the handwritten word recognition task. For example, the literature mentions recognizers with accuracy for lexicons of sizes 10, 100, 1,000, and so forth, implying that the difficulty of the task increases (and, hence, recognition accuracy decreases) with increasing lexicon sizes across recognizers. Lexicon density is an alternate measure which is quite dependent on the recognizer. There are many applications such as addressinterpretation where such a recognizer dependent measure can be useful. We have conducted experiments with two different types of recognizers. A segmentation-based and a grapheme-based recognizer have been selected to show how the measure of lexicon density can be developed in general for any recognizer. Experimental results show that the lexicon density measure described is more suitable than lexicon size or a simple string edit distance.</p>
Classifier combination, handwritten word recognizer, lexicon density, performance prediction, edit distances.

V. Govindaraju, P. Slavík and H. Xue, "Use of Lexicon Density in Evaluating Word Recognizers," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 24, no. , pp. 789-800, 2002.
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