# Historical OCR Text Quality Analysis and Post-correction


Instructor: Sindhu Kutty;
Sponsor: Dr. John Dillon, Dr. Dan Hepp (ProQuest).

Motivation
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Predict and Improve scanned-OCR of historical texts using machine learning methods.

Contribution
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- Create a parallel dataset between scanned-OCR New York Times news passages and corresponding human-generated clean texts;
- Develop a synthetic dataset using human-generated clean data to simulate the OCR-specific mistakes;
- Provide a unsupervised scanned-OCR text cleanliness predictor trained with basic language features;
- Fine-tune T5-base model on the end-to-end OCR sentence-level correction task.

Shared Slides
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- [Shared Slides](https://docs.google.com/presentation/d/1NZffeTILI0LmeixYr9SBmj_U7Ek-mJ5Q/edit?usp=sharing&ouid=111081196382689176167&rtpof=true&sd=true)
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If you are interested in detailed information, please email the author :)

###### tags: `Research Project` `OCR Post-Correction`