Depending on the quality of the original document, Optical Character Recognition (OCR) can produce a range of errors -- from erroneous letters to additional and spurious blank spaces. We applied a sequence-to-sequence machine translation system to correct word-segmentation OCR errors in scientific texts from the ACL collection with an estimated precision and recall above 0.95 on test data. We present the correction process and results.
@InProceedings{NASTASE18.114, author = {Vivi Nastase and Julian Hitschler}, title = "{Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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