https://github.com/cjbarrie/noisy-ocr-benchmark
Replication materials for the article "OCR with Tesseract, Amazon Textract, and Google Document AI: A Benchmarking Experiment"
Science Score: 10.0%
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Low similarity (5.3%) to scientific vocabulary
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Replication materials for the article "OCR with Tesseract, Amazon Textract, and Google Document AI: A Benchmarking Experiment"
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- Host: GitHub
- Owner: cjbarrie
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- Size: 60.1 MB
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# An OCR Benchmarking Experiment This repository holds replication materials for the manuscript ["OCR with Tesseract, Amazon Textract, and Google Document AI: A Benchmarking Experiment"](https://osf.io/preprints/socarxiv/6zfvs). It contains: - The .RMD file of the manuscript with R code for all the figures. - 51,304 .TXT files with the text output from all the OCR processing requests. - A .CSV file with word and character accuracy rates for all the OCR output. The image test materials reside in a separate Zenodo repository as the ["Noisy OCR Dataset"](https://zenodo.org/record/5068735) (NOD). From the abstract: >This article reports a benchmarking experiment comparing the performance of Tesseract, Amazon Textract, and Google Document AI on images of English and Arabic text. English-language book scans (n=322) and Arabic-language article scans (n=100) were replicated 43 times with different types of artificial noise for a corpus of 18,568 documents, generating 51,304 process requests. Document AI delivered the best results, and the server-based processors (Textract and Document AI) were substantially more accurate than Tesseract, especially on noisy documents. Accuracy for English was considerably better than for Arabic. Core results: \ \ 
Owner
- Name: Christopher Barrie
- Login: cjbarrie
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- Company: University of Edinburgh
- Website: https://cjbarrie.com
- Twitter: cbarrie
- Repositories: 7
- Profile: https://github.com/cjbarrie
Lecturer in Computational Sociology, University of Edinburgh.