Emergency medicine, disease surveillance, and informatics

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 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. An approach for segmenting handwritten text is also presented. Stroke analyses are performed and image primitives are extracted for word detection. A heuristics-based approach, involving gap spacing, height transitions, and the average stroke width of the writer is used in detecting word boundaries. Experiments show perfect segmentation of 69%, outperforming the more tested and proven algorithms by as much as 15%.