How to create a corpus of machine-readable texts: challenges and solutions
Download as PPTX, PDF0 likes78 views
This document discusses optical character recognition (OCR) and its use and challenges in digitizing historical texts for research. It begins by defining OCR and how it works, then discusses its history and development from the 1950s to present. Current OCR tools use machine learning to improve accuracy, but open-source tools can be difficult to install and use. The document evaluates several popular OCR tools and services available to researchers and provides tips on integrating OCR into one's own workflow.
How to create a corpus of machine-readable texts: challenges and solutions
- 1. How to create a corpus of machine-readable texts: challenges and solutions
- 2. What is OCR and how does it work? Definition of OCR according to the Oxford Dictionary of Computer Science, p. 379: „OCR = optical character recognition; a process in which a machine scans, recognizes, and encodes information printed or typed in alphanumerical characters. (…) OCR software is now readily available for many low-cost scanners giving good recognition rates for printed material using the Latin alphabet. The more difficult problems posed by other character sets and handwriting are areas of ongoing research.“
- 3. When was OCR software invented? mid-1970s: OCR A font and OCR B font (similar to normal letter-press appearance) Ca. 1955: early OCR devices only recognised limited set of characters in machine-optimised font
- 4. Do we encounter OCR in everyday-life? High accuracy rates have popularised OCR in the following areas: • banking (machines „reading“ paper cheques and transfer forms) • public administration • health-care (e.g. machine-readable precriptions) NOTE: In cases where absolute perfection is needed, OCR A and OCR B fonts are still used. If sensitive information is handled, OCR technology can be combined with the so-called MICR technology (magnetic-ink character recognition) checking the legitimacy or originality of paper documents.
- 5. Are humanities tools using OCR? Google Books: full-text search + highlighting of text results HathiTrust full-text view
- 6. What are ORC problems in historical research? „Hannoverisches Magazin”, 1776
- 7. Best historical OCR results: texts in standardised formats (e.g. periodicals)
- 8. Improving results for minority languages and old fonts – an on-going challenge
- 9. Recent innovation: merging OCR and handwriting recognition technologies (HWR/HTR) “Handwriting recognition (HWR), also known as Handwritten Text Recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. The image of the written text may be sensed "off line" from a piece of paper by optical scanning (optical character recognition) or intelligent word recognition. Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface, a generally easier task as there are more clues available. A handwriting recognition system handles formatting, performs correct segmentation into characters, and finds the most plausible words.” Wikipedia.org
- 10. The machine learning revolution in OCR
- 11. How does machine learning work? Cf. Stanford OCR pipeline: • text detection (layout recognition) • character segmentation (using „sliding window“ technique) • character classification • spell correction (http://doremi2016.logdown.com/posts/ 2017/01/20/standford-machine- learning-photo-ocr-machine-learning- pipeline)
- 12. New OCR tools based on machine learning E.g. OCR-D project in Germany: • improved visual character recognition • context analysis of n-grams • trainer feedback to exclude potential mistakes
- 13. Current range of OCR-tools for researchers • Transkribus.eu (free of charge, cloud-based, each user contributes training data to the community) • OCR4all (free command-line OCR software for desktop-installation, difficult set-up, does not run smoothly on Windows) • KRAKEN (Python package for OCR, usage not monitored, data do not need to be shared with developers or others users) • ABBYY FineReader (one of the most popular proprietary OCR tools) • Tesseract (originally developed as proprietary software at Hewlett Packard labs in England and the US, released as open source in 2005, supported by Google since 2006, available for Linux as well as Windows and Mac OS X, high pre-processing requirements) • PICCL/TICCL (free corpus building and corpus clean-up system performing spelling correction and OCR post-correction, developed for LINUX, requires virtual machine on Windows)
- 14. GBV-Verbund: Intranda OCR Service
- 15. And the development continues…
- 16. PROs and CONs of open-source OCR software: CONs: • takes up a lot of storage space • difficult installation • often limited performance on Windows and Mac) • usually requires command-line operation (no GUI) • conducting own training can be time-consuming • copyright issues if software provider requires you to ingest your (training) data into a public pool PROs: • flexible integration of historical texts in different digital formats • adaptable to multiple languages and new fonts / page layouts Photo by Luca Bravo on Unsplash
- 17. How reliable are current OCR tools? Results based on an OCR test based on the US driver‘s licence, published on September 18, 2019: https://mobidev.biz/blog/ocr-machine-learning- implementation
- 18. How can we integrate OCR into our own workflow? Export scan as PDF or image files to perform OCR! Analyse non-coded plain text with topic modelling or stylometry tools not requiring structured data! Code information (e.g. in XML/TEI or JSON) and use software to analyse networks between specific tagged entities or visualise geographic data! Export scan as PDF or image file to let humans extract metadata and transcribe the text! Original manuscript or print Use transcriptions to train OCR- software and improve results for similar sources (e.g. issues of the same newspaper)! Perform quantitative analysis on more texts in less time and generate more reliable results!
- 19. Testing a cloud-based OCR tool: transkribus.eu