OcrV1, Main, Exploration, bibRecord, 000375

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

Identifieur interne : 000375 ( Main/Exploration ); précédent : 000374; suivant : 000376

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

Auteurs : Luke V. Rasmussen [États-Unis] ; Peggy L. Peissig [États-Unis] ; Catherine A. Mccarty [États-Unis] ; Justin Starren [États-Unis]

Source :

Journal of the American Medical Informatics Association : JAMIA [ 1067-5027 ] ; 2011.

RBID : PMC:3392858

Abstract

Background

Although the penetration of electronic health records is increasing rapidly, much of the historical medical record is only available in handwritten notes and forms, which require labor-intensive, human chart abstraction for some clinical research. The few previous studies on automated extraction of data from these handwritten notes have focused on monolithic, custom-developed recognition systems or third-party systems that require proprietary forms.

Methods

We present an optical character recognition processing pipeline, which leverages the capabilities of existing third-party optical character recognition engines, and provides the flexibility offered by a modular custom-developed system. The system was configured and run on a selected set of form fields extracted from a corpus of handwritten ophthalmology forms.

Observations

The processing pipeline allowed multiple configurations to be run, with the optimal configuration consisting of the Nuance and LEADTOOLS engines running in parallel with a positive predictive value of 94.6% and a sensitivity of 13.5%.

Discussion

While limitations exist, preliminary experience from this project yielded insights on the generalizability and applicability of integrating multiple, inexpensive general-purpose third-party optical character recognition engines in a modular pipeline.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392858

DOI: 10.1136/amiajnl-2011-000182
PubMed: 21890871
PubMed Central: 3392858

Affiliations:

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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Although the penetration of electronic health records is increasing rapidly, much of the historical medical record is only available in handwritten notes and forms, which require labor-intensive, human chart abstraction for some clinical research. The few previous studies on automated extraction of data from these handwritten notes have focused on monolithic, custom-developed recognition systems or third-party systems that require proprietary forms.</p>
</sec>
<sec><title>Methods</title>
<p>We present an optical character recognition processing pipeline, which leverages the capabilities of existing third-party optical character recognition engines, and provides the flexibility offered by a modular custom-developed system. The system was configured and run on a selected set of form fields extracted from a corpus of handwritten ophthalmology forms.</p>
</sec>
<sec><title>Observations</title>
<p>The processing pipeline allowed multiple configurations to be run, with the optimal configuration consisting of the Nuance and LEADTOOLS engines running in parallel with a positive predictive value of 94.6% and a sensitivity of 13.5%.</p>
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<p>While limitations exist, preliminary experience from this project yielded insights on the generalizability and applicability of integrating multiple, inexpensive general-purpose third-party optical character recognition engines in a modular pipeline.</p>
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Development of an optical character recognition pipeline for handwritten form fields from an electronic health record

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

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