A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
The String-to-String Correction Problem
Journal of the ACM (JACM)
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We have developed the notion of lexicon density as the true metric to measure expected recognizer accuracy. This metric has a variety of applications, among them evaluation of recognition results, static or dynamic recognizer selection, or dynamic combination of recognizers. We show that the performance of word recognizers increases as lexicon density decreases and that the relationship between the performance and lexicon density is independent of lexicon size. Our claims are supported by extensive experimental validation data.