A Benchmark and Dataset for Post-OCR text correction in Sanskrit

A Benchmark and Dataset for Post-OCR text correction in Sanskrit
Ayush Maheshwari, Nikhil Singh, Amrith Krishna and Ganesh Ramakrishnan
Findings of EMNLP 2022

Post-edited data

• *_devanagari.csv refers to train, test and validation split of manually post-edited OCR data
• ood-test.csv refers to out-of-domain test set consisting of 500 sentences as described in Section 4.1 of the paper.

dev-transliterate-scripts/

• contains scripts to transliterate words from from SLP1 to Dev and vice-versa

OCR images and their annotation

• OCR-Images-Annotation/ folder contains books containing test set of 500 images and their corresponding groundtruth.
• BHS refers to Brahmastura Bhashyam
• GG refers to Grahalaghava of Ganesh Daivajna
• GOS refers to Goladhyaya

Training Scripts

• Training scripts are present in the train-scripts directory

Calculate CER, WER

• preds/ folder contains predictions and GT for the 500 sentences in out-of-domain test set
• To calculate, run pip install fastwer
• python word_count_cer.py <cer/wer>

Citation:

@inproceedings{maheshwari2022benchmark,
  title={A Benchmark and Dataset for Post-OCR text correction in Sanskrit},
  author={Maheshwari, Ayush and Singh, Nikhil and Krishna, Amrith and Ramakrishnan, Ganesh},
  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2022},
  pages={6258--6265},
  year={2022}
}