Binarization and cleanup of handwritten text from carbon copy medical form images |
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| Authors: | Robert Milewski Venu Govindaraju |
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| Affiliation: | 1. Department of Cardiology, University of Arkansas for Medical Sciences and the Central Arkansas Veterans Healthcare System, Little Rock, AR, United States;2. Department of Endocrinology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People’s Republic of China;3. Department of Cardiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People’s Republic of China;1. Shanghai Key Lab of Modern Optical System, and Engineering Research Center of Optical Instrument and System, Ministry of Education, University of Shanghai for Science and Technology, Shanghai 200093, China;2. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;3. School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China;1. Future Institute of Engineering & Management, Kolkata, India;2. Jadavpur University, Kolkata, India;3. IIEST, Howrah, India;1. Interactive Research and Development, Karachi, Pakistan;2. Harvard Medical School Center for Global Health Delivery–Dubai, UAE |
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| Abstract: | This paper presents a methodology for separating handwritten foreground pixels, from background pixels, in carbon copied medical forms. Comparisons between prior and proposed techniques are illustrated. This study involves the analysis of the New York State (NYS) Department of Health (DoH) Pre-Hospital Care Report (PCR) [Western Regional Emergency Medical Services, Bureau of Emergency Medical Services, New York State (NYS) Department of Health (DoH), Prehospital Care Report v4.] which is a standard form used in New York by all Basic and Advanced Life Support pre-hospital health care professionals to document patient status in the emergency environment. The forms suffer from extreme carbon mesh noise, varying handwriting pressure sensitivity issues, and smudging which are further complicated by the writing environment. Extraction of handwriting from these medical forms is a vital step in automating emergency medical health surveillance systems. |
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