Title :
On the dependence of handwritten word recognizers on lexicons
Author :
Xue, Hanhong ; Govindaraju, Venu
Author_Institution :
Dept. of Comput. Sci. & Eng., State Univ. of New York, USA
fDate :
12/1/2002 12:00:00 AM
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.
Keywords :
dictionaries; document image processing; handwritten character recognition; optical character recognition; performance evaluation; statistical analysis; character recognition; document processing; experiments; handwritten word recognition; lexicons; multiple regression model; performance model; quantitative methodology; Birth disorders; Character recognition; Handwriting recognition; Helium; Image recognition; Predictive models; Testing; Training data; Venus; Vocabulary;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2002.1114848