Coding and information theory (2nd ed.)
Coding and information theory (2nd ed.)
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)
On the Dependence of Handwritten Word Recognizers on Lexicons
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Human Interactive Proof Algorithm Using Handwriting Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A Lexicon Reduction Strategy in the Context of Handwritten Medical Forms
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
WAMUS'05 Proceedings of the 5th WSEAS International Conference on Wavelet Analysis and Multirate Systems
Leveraging cognitive factors in securing WWW with CAPTCHA
WebApps'10 Proceedings of the 2010 USENIX conference on Web application development
Visual CAPTCHA with handwritten image analysis
HIP'05 Proceedings of the Second international conference on Human Interactive Proofs
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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.