RoundTripOCR: A Data Generation Technique for Enhancing OCR Error Correction in Low-Resource Devanagari Languages
Authors: Harshvivek Kashid, Pushpak Bhattacharyya


Reviewer paper summaries: The authors proposed RoundTripOCR to generate synthetic training data for OCR error correction. RoundTripOCR first converts clean texts to images and then converts the images back to texts that may contain errors using Tesseract OCR. The pairs of clean and error texts are used for training and evaluation. The authors also fine-tuned mBART using synthetic data and conducted experiments on six Devanagari languages.
Summary of strengths: -	The approach is simple and could be applied to other languages.
-	The authors explain the types of OCR errors.
-	The authors released some samples of the synthetic noises to the public.
Summary of weaknesses: -	The robustness of the approach is unclear since the training and test data come from the same distribution. The authors did not demonstrate how many errors in the test data were also present in the training data. How robust are the models to unseen errors or errors that occur when OCR is applied to scanned documents?
-	There is no comparison with existing approaches, which can be found in Nguyen et al., 2021. Specifically, this approach is OCR-model specific, whereas existing approaches such as randomly deleting, inserting, and substituting characters from a given word are model agnostic.
-	The novelty of the approach is unclear, and the authors did not discuss existing works on synthetic data for training OCR post-processing, as found in the survey paper by Nguyen et al., 2021.
Comments, suggestions and typos: -	A related works section would be beneficial, especially for discussing existing works on synthetic data for OCR post-processing. This would help readers follow this line of research.
-	Comparing with existing approaches, as mentioned above, would make the approach more convincing.
-	It would be helpful if the authors could show the error distribution of the training and test data, as well as the distribution of unseen errors in the test data.
Overall assessment scores: 2.5