A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lexicon reduction using key characters in cursive handwritten words
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Offline General Handwritten Word Recognition Using an Approximate BEAM Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of Lexicon Density in Evaluating Word Recognizers
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Dependence of Handwritten Word Recognizers on Lexicons
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lexicon Reduction in an HMM-Framework Based on Quantized Feature Vectors
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Using lexical knowledge for the recognition of poorly written words
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwriting Analysis of Pre-Hospital Care Reports
CBMS '04 Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
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Traditional handwriting recognition algorithms rely heavily on small lexicons and clean word images. Unfortunately, emergency medical documents do not satisfy either of these conditions. This is a significant road-block that is hampering efforts to rapidly convert valuable offline healthcare handwriting data into digital content that can be efficiently mined for information. This paper describes a strategy whereby given an image representing a noisy handwritten word from a medical document, and a large lexicon consisting of English, medical and pharmacological words, symbols, abbreviations and acronyms, significantly reduces the size of the lexicon while keeping the unknown desired entry within the lexicon. The approach combines geometric interpretations of the word image along with contextual inference of concepts to reduce lexicons for word recognition. The data extracted can then be efficiently and securely disseminated for epidemiological and outbreak detection/analysis. Experimental results on NY State PCR forms are reported.