Automatic Assessment of OCR Quality in Historical Documents

Authors

  • Anshul Gupta Texas A&M University
  • Ricardo Gutierrez-Osuna Texas A&M University
  • Matthew Christy Texas A&M University
  • Boris Capitanu University of Illinois at Urbana-Champaign
  • Loretta Auvil University of Illinois at Urbana-Champaign
  • Liz Grumbach Texas A&M University
  • Richard Furuta Texas A&M University
  • Laura Mandell Texas A&M University

Keywords:

optical character recognition, machine learning, digital humanities, document triage

Abstract

Mass digitization of historical documents is a challenging problem for optical character recognition (OCR) tools. Issues include noisy backgrounds and faded text due to aging, border/marginal noise, bleed-through, skewing, warping, as well as irregular fonts and page layouts. As a result, OCR tools often produce a large number of spurious bounding boxes (BBs) in addition to those that correspond to words in the document. This paper presents an iterative classification algorithm to automatically label BBs (i.e., as text or noise) based on their spatial distribution and geometry. The approach uses a rule-base classifier to generate initial text/noise labels for each BB, followed by an iterative classifier that refines the initial labels by incorporating local information to each BB, its spatial location, shape and size. When evaluated on a dataset containing over 72,000 manually-labeled BBs from 159 historical documents, the algorithm can classify BBs with 0.95 precision and 0.96 recall. Further evaluation on a collection of 6,775 documents with ground-truth transcriptions shows that the algorithm can also be used to predict document quality (0.7 correlation) and improve OCR transcriptions in 85% of the cases.

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Published

2015-02-18

How to Cite

Gupta, A., Gutierrez-Osuna, R., Christy, M., Capitanu, B., Auvil, L., Grumbach, L., Furuta, R., & Mandell, L. (2015). Automatic Assessment of OCR Quality in Historical Documents. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/9487

Issue

Section

Main Track: Machine Learning Applications