Learning on the fly: a font-free approach toward multilingual OCR

Original Paper

DOI: 10.1007/s10032-011-0164-6

Cite this article as:
Kae, A., Smith, D.A. & Learned-Miller, E. IJDAR (2011) 14: 289. doi:10.1007/s10032-011-0164-6
  • 110 Downloads

Abstract

Despite ubiquitous claims that optical character recognition (OCR) is a “solved problem,” many categories of documents continue to break modern OCR software such as documents with moderate degradation or unusual fonts. Many approaches rely on pre-computed or stored character models, but these are vulnerable to cases when the font of a particular document was not part of the training set or when there is so much noise in a document that the font model becomes weak. To address these difficult cases, we present a form of iterative contextual modeling that learns character models directly from the document it is trying to recognize. We use these learned models both to segment the characters and to recognize them in an incremental, iterative process. We present results comparable with those of a commercial OCR system on a subset of characters from a difficult test document in both English and Greek.

Keywords

Character recognition OCR Cryptogram Font-free models Multilingual OCR 

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Andrew Kae
    • 1
  • David A. Smith
    • 1
  • Erik Learned-Miller
    • 1
  1. 1.Department of Computer ScienceUniversity of Massachusetts AmherstAmherstUSA

Personalised recommendations