OCRfixr 1.3.1

A contextual spellchecker for OCR output

OCRfixr

OVERVIEW

This project aims to help automate the challenging work of manually correcting OCR output from Distributed Proofreaders' book digitization projects

Correcting OCR Misreads

OCRs can sometimes mistake similar-looking characters when scanning a book. For example, "l" and "1" are easily confused, potentially causing the OCR to misread the word "learn" as "1earn".

As written in book:

"The birds flevv south"

Corrected text:

"The birds flew south"

How OCRfixr Works:

OCRfixr fixes misreads by checking 1) possible spell corrections against the 2) local context of the word. For example, here's how OCRfixr would evaluate the following OCR mistake:

As written in book:

"Days there were when small trade came to the stoie. Then the young clerk read."

Method	Plausible Replacements
Spellcheck (symspellpy)	stone, store, stoke, stove, stowe, stole, soie
Context (BERT)	market, shop, town, city, store, table, village, door, light, markets, surface, place, window, docks, area

Since there is match for both a plausible spellcheck replacement and that word reasonably matches the context of the sentence, OCRfixr updates the word.

Corrected text:

"Days there were when small trade came to the store. Then the young clerk read."

Using OCRfixr

The package can be installed using pip.

pip install OCRfixr

By default, OCRfixr only returns the original string, with all changes incorporated:

>>> from ocrfixr import spellcheck

>>> text = "The birds flevv south"
>>> spellcheck(text).fix()
'The birds flew south'

Use return_fixes to also include all corrections made to the text, with associated counts for each:

>>> spellcheck(text, return_fixes = "T").fix()
['The birds flew south', {("flevv","flew"):1}]

For longer texts, use changes_by_paragraph to show each change in local context. This will only display the paragraphs that had updates made to them, for ease of review:

>>> text = "The birds flevv down\n south, bvt wefe quickly apprehended\n by border patrol agents"
>>> spellcheck(text, changes_by_paragraph = "T").fix()
[["The birds flew down\n",{"flevv":"flew"}], 
[" south, but were quickly apprehended\n", {"bvt":"but", "wefe":"were"}]]

(Note: OCRfixr resets its BERT context window at the start of each new paragraph, so splitting by paragraph may be a useful debug feature)

Interactive Mode

OCRfixr also has an option for the user to interactively accept/reject suggested changes to the text:

>>> text = "The birds flevv down\n south, bvt wefe quickly apprehended\n by border patrol agents"
>>> spellcheck(text, interactive = "T").fix()

Each suggestion provides the local context around the garbled text, so that the user can determine if the suggestion fits.

>>> ### User accepts change to "flevv", but rejects change to "quikly" in GUI
'The birds flew down\n south and were quikly apprehended'

This returns the text with all accepted changes reflected. All rejected suggestions are left as-is in the text.

Avoiding "Damn You, Autocorrect!"

By design, OCRfixr is change-averse:

• If spellcheck/context do not line up, no update is made.
• Likewise, if there is >1 word that lines up for spellcheck/context, no update is made.
• Only the top 15 context suggestions are considered, to limit low-probability matches.
• Proper nouns (anything starting with a capital letter) are not evaluated for spelling.

Word context is drawn from all sentences in the current paragraph, to maximize available information, while also not bogging down the BERT model.

Credits

• symspellpy powers spellcheck suggestions
• transformers does the heavy lifting for BERT context modelling.
• SCOWL word list is Copyright 2000-2019 by Kevin Atkinson.
• This project was created to help Distributed Proofreaders. Support them here: https://www.pgdp.net/c/