Abstract
Note taking is a common way for physicians to collect information from their patients in medical inquiries and diagnoses. Many times, when describing the pathology in medical records, a physician also draws diagrams and/or anatomical sketches along with the free-text narratives. The ability to understand unstructured handwritten texts and drawings in patient record could lead to implementation of automated patient record systems with more natural interfaces than current highly structured systems. The first and crucial step in automated processing of free-hand medical records is to segment the record into handwritten text and drawings, so that appropriate recognizers can be applied to different regions. This paper presents novel algorithms that separate text from non-text strokes in an on-line handwritten patient record. The algorithm is based on analyses of spatio-temporal graphs extracted from an on-line patient record and support vector machine (SVM) classification. Experiments demonstrate that the proposed approach is effective and robust.
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Waranusast, R., Haddawy, P., Dailey, M. (2009). Segmentation of Text and Non-text in On-Line Handwritten Patient Record Based on Spatio-Temporal Analysis. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds) Artificial Intelligence in Medicine. AIME 2009. Lecture Notes in Computer Science(), vol 5651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02976-9_47
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DOI: https://doi.org/10.1007/978-3-642-02976-9_47
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