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- W2137699896 abstract "The performance of any word recognizer depends on the lexicon presented. Usually, large lexicons or lexicons containing similar entries pose difficulty for recognizers. However, the literature lacks any quantitative methodology of capturing the precise dependence between word recognizers and lexicons. This paper presents a performance model that views word recognition as a function of character recognition and statistically discovers the relation between a word recognizer and the lexicon. It uses model parameters that capture a recognizer's ability of distinguishing characters (of the alphabet) and its sensitivity to lexicon size. These parameters are determined by a multiple regression model which is derived from the performance model. Such a model is very useful in comparing word recognizers by predicting their performance based on the lexicon presented. We demonstrate the performance model with extensive experiments on five different word recognizers, thousands of images, and tens of lexicons. The results show that the model is a good fit not only on the training data but also in predicting the recognizers' performance on testing data." @default.
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- W2137699896 date "2002-12-01" @default.
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- W2137699896 title "On the dependence of handwritten word recognizers on lexicons" @default.
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- W2137699896 doi "https://doi.org/10.1109/tpami.2002.1114848" @default.
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