Upcycle Your OCR: Reusing OCRs for Post-OCR Text Correction in Romanised Sanskrit
arXiv:1809.02147
Abstract
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.
arXiv commentThis paper has been accepted as a full paper in the SIGNLL Conference on Computational Natural Language Learning (CoNLL), 2018. The code, data and the supplementary material is available at https://github.com/majumderb/sanskrit-ocr
References in corpus (5)
- Sequence to Sequence Learning with Neural Networks
- Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
- Effective Approaches to Attention-based Neural Machine Translation
- Incorporating Copying Mechanism in Sequence-to-Sequence Learning
- Recognition of Non-Compound Handwritten Devnagari Characters using a Combination of MLP and Minimum Edit Distance