Development of an optical character recognition pipeline for handwritten form fields from an electronic health record

Journal of the American Medical Informatics Association

Volumn 19, Issue E1, 2012, Pages

  Rasmussen, Luke V ^a   Peissig, Peggy L ^a   McCarty, Catherine A ^b   Starren, Justin ^a,c  

^a MARSHFIELD CLINIC   (United States)

^b ESSENTIA INSTITUTE OF RURAL HEALTH   (United States)

^c NORTHWESTERN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; AUTOMATION; BIOMEDICAL ENGINEERING; CATARACT; DISEASE SEVERITY; ELECTRONIC MEDICAL RECORD; HANDWRITING; HUMAN; MEDICAL DOCUMENTATION; OPTICAL CHARACTER RECOGNITION PROCESSING PIPELINE; SENSITIVITY AND SPECIFICITY;

AUTOMATIC DATA PROCESSING; ELECTRONIC HEALTH RECORDS; HUMANS;

EID: 84863539091     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2011-000182     Document Type: Article

References (17)

1. The eMERGE Network: electronic Medical Records and Genomics. http://www.gwas.org(accessed 19 May 2011).
2. Kho AN, Pacheco JA, Peissig PL, et al. Electronic medical records for genetic research: results of the eMERGE consortium. Sci Transl Med 2011;3:79re1.
3. Biondich PG, Overhage JM, Dexter PR, et al. A modern optical character recognition system in a real world clinical setting: some accuracy and feasibility observations. Proc AMIA Symp 2002:56-60.
4. Bussmann H, Wester CW, Ndwapi N, et al. Hybrid data capture for monitoring patients on highly active antiretroviral therapy (HAART) in urban Botswana. Bull World Health Organ 2006;84:127-31.
5. Titlestad G. Use of document image processing in cancer registration: how and why? Medinfo 1995:462.
6. Govindaraju V. Emergency medicine, disease surveillance, and informatics. Proc of the 2005 National Conference on Digital Government Research, Digital Government Society of North America, 2005:167-8.
7. Milewski R, Govindaraju V. Handwriting analysis of pre-hospital care reports. Proc CBMS, IEEE Computer Society, Los Alamitos, CA, 2004:428-33.
8. Piasecki M, Broda B. Correction of Medical Handwriting OCR Based on Semantic Similarity, Intelligent Data Engineering and Automated Learning-IDEAL 2007. Vol. 4881. Berlin, Heidelberg: Springer Berlin/Heidelberg, 2007:437-46.
9. Savova GK, Masanz JJ, Ogren PV, et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 2010;17:507-13.
10. Cunningham H, Maynard D, Bontcheva K, et al. GATE: a framework and graphical development environment for robust NLP tools and applications. Proc of the 40th Assoc for Comp Ling (ACL'02); Philadelphia, July 2002.
11. Ghezzi C, Jazayeri M, Mandrioli D. Fundamentals of Software Engineering. 2nd edn. Harlow: Prentice Hall, 2002.
12. McCarty CA, Wilke RA, Giampietro PF, et al. Marshfield Clinic Personalized Medicine Research Project (PMRP): design, methods and recruitment for a large populationbased biobank. Per Med 2005;2:49-79.
13. McCarty CA, Peissig P, Caldwell MD, et al. The Marshfield Clinic Personalized Medicine Research Project: 2008 scientific update and lessons learned in the first 6 years. Per Med 2008;5:529-42.
14. Yoo JY, Kim MK, Han SY, et al. Line removal and restoration of handwritten characters on the form documents. ICDAR'97, IEEE Computer Society, Los Alamitos, CA, 1997:128-31.
15. Tesseract-OCR engine homepage. Google Code. http://code.google.com/p/tesseract-ocr/ (accessed 19 May 2011).
16. LEADTOOLS Developer's Toolkit. ICR Engine. http://www.leadtools.com/sdk/ocr/icr.htm (accessed 19 May 2011).
17. Nuance. OmniPage CSDK. http://www.nuance.com/for-business/by-product/omnipage/csdk/index.htm (accessed 19 May 2011).