OCR as a Service: An Experimental Evaluation of Google Docs OCR, Tesseract, ABBYY FineReader, and Transym

  • Ahmad P. Tafti
  • Ahmadreza Baghaie
  • Mehdi Assefi
  • Hamid R. Arabnia
  • Zeyun Yu
  • Peggy Peissig
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10072)


Optical character recognition (OCR) as a classic machine learning challenge has been a longstanding topic in a variety of applications in healthcare, education, insurance, and legal industries to convert different types of electronic documents, such as scanned documents, digital images, and PDF files into fully editable and searchable text data. The rapid generation of digital images on a daily basis prioritizes OCR as an imperative and foundational tool for data analysis. With the help of OCR systems, we have been able to save a reasonable amount of effort in creating, processing, and saving electronic documents, adapting them to different purposes. A set of different OCR platforms are now available which, aside from lending theoretical contributions to other practical fields, have demonstrated successful applications in real-world problems. In this work, several qualitative and quantitative experimental evaluations have been performed using four well-know OCR services, including Google Docs OCR, Tesseract, ABBYY FineReader, and Transym. We analyze the accuracy and reliability of the OCR packages employing a dataset including 1227 images from 15 different categories. Furthermore, we review the state-of-the-art OCR applications in healtcare informatics. The present evaluation is expected to advance OCR research, providing new insights and consideration to the research area, and assist researchers to determine which service is ideal for optical character recognition in an accurate and efficient manner.


Optical Character Recognition Healthcare Informatics Optical Character Recognition System Optical Character Recognition Package Optical Character Recognition Engine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors of the paper wish to thank Anne Nikolai at Marshfield Clinic Research Foundation for her valuable contributions in manuscript preparation. We also thank two anonymous reviewers for their useful comments on the manuscript.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Biomedical Informatics Research CenterMarshfield Clinic Research FoundationMarshfieldUSA
  2. 2.Department of Electrical EngineeringUniversity of Wisconsin-MilwaukeeMilwaukeeUSA
  3. 3.Department of Computer ScienceUniversity of GeorgiaAthensUSA
  4. 4.Department of Computer ScienceUniversity of Wisconsin-MilwaukeeMilwaukeeUSA

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