We propose a post-OCR text correction approach for digitising texts in
Romanised Sanskrit. Owing to the lack of resources our approach uses OCR models
trained for other languages written in Roman. Currently, there exists no
dataset available for Romanised Sanskrit OCR. So, we bootstrap a dataset of 430
images, 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 percentage
increase of 7.69 in Character Recognition Rate (CRR) than the current state of
the 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 it
obtains a CRR of 87.01% from an OCR output with CRR of 35.76% for one of the
dataset settings. A human judgment survey performed on the models shows that
our proposed model results in predictions which are faster to comprehend and
faster to improve for a human than the other systems.

针对罗马化梵文文本数字化的后 OCR 文本纠正方法，使用其他罗马字母语言的 OCR 模型进行训练，通过合成数据生成来训练模型，并使用一种复制机制来提高字符识别率。实验结果表明，该模型在解决单调序列 - 序列任务方面比当前最先进的模型结果提高了 7.69%，能够有效地减少 OCR 产生的错误。此外，该模型的预测结果可被人类更快地理解和改进。