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We propose a post-OCR text correction approach for digitising texts inRomanised Sanskrit. Owing to the lack of resources our approach uses OCR modelstrained for other languages written in Roman. Currently, there exists nodataset available for Romanised Sanskrit OCR. So, we bootstrap a dataset of 430images, scanned in two different settings and their corresponding ground truth.For training, we synthetically generate training images for both the settings.We find that the use of copying mechanism (Gu et al., 2016) yields a percentageincrease of 7.69 in Character Recognition Rate (CRR) than the current state ofthe art model in solving monotone sequence-to-sequence tasks (Schnober et al.,2016). We find that our system is robust in combating OCR-prone errors, as itobtains a CRR of 87.01% from an OCR output with CRR of 35.76% for one of thedataset settings. A human judgment survey performed on the models shows thatour proposed model results in predictions which are faster to comprehend andfaster to improve for a human than the other systems.

Upcycle Your OCR: Reusing OCRs for Post-OCR Text Correction in Romanised Sanskrit