Summary of the paper

Title Using SMT for OCR Error Correction of Historical Texts
Authors Haithem Afli, Zhengwei Qiu, Andy Way and Páraic Sheridan
Abstract A trend to digitize historical paper-based archives has emerged in recent years, with the advent of digital optical scanners. A lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into electronic versions that can be manipulated by a computer. For this purpose, Optical Character Recognition (OCR) systems have been developed to transform scanned digital text into editable computer text. However, different kinds of errors in the OCR system output text can be found, but Automatic Error Correction tools can help in performing the quality of electronic texts by cleaning and removing noises. In this paper, we perform a qualitative and quantitative comparison of several error-correction techniques for historical French documents. Experimentation shows that our Machine Translation for Error Correction method is superior to other Language Modelling correction techniques, with nearly 13% relative improvement compared to the initial baseline.
Topics Optical Character Recognition, Language Modelling, Machine Translation, SpeechToSpeech Translation
Full paper Using SMT for OCR Error Correction of Historical Texts
Bibtex @InProceedings{AFLI16.280,
  author = {Haithem Afli and Zhengwei Qiu and Andy Way and Páraic Sheridan},
  title = {Using SMT for OCR Error Correction of Historical Texts},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
 }
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