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Deep Statistical Analysis of OCR Errors for Effective Post-OCR Processing by Thi-Tuyet-Hai Nguyen (University of La Rochelle), Adam Jatowt (Kyoto University), Mickael Coustaty (University of La Rochelle), Nhu-Van Nguyen (University of La Rochelle), Antoine Doucet (University of La Rochelle) is a scholarly article available to read on EtoBox.

What is Deep Statistical Analysis of OCR Errors for Effective Post-OCR Processing about?

Post-OCR is an important processing step that follows optical character recognition (OCR) and is meant to improve the quality of OCR documents by detecting and correcting residual errors. This paper describes the results of a statistical analysis of OCR errors on four document collections. Five aspects related to general OCR errors are studied and compared with human-generated misspellings, including edit operations, length effects, erroneous character positions, real-word vs. non-word errors, and word boundaries. Based on the observations from the analysis we give several suggestions related to the design and implementation of effective OCR post-processing approaches.

Author

Thi-Tuyet-Hai Nguyen (University of La Rochelle), Adam Jatowt (Kyoto University), Mickael Coustaty (University of La Rochelle), Nhu-Van Nguyen (University of La Rochelle), Antoine Doucet (University of La Rochelle)

Publisher

IEEE

Published

2019

Language

EN