Metadata-Version: 2.1
Name: OCRfixr
Version: 1.0.0
Summary: A contextual spellchecker for OCR output
Home-page: https://github.com/ja-mcm/ocrfixr
Author: Jack McMahon
Author-email: OCRfixr@mcmahon.work
License: GNU General Public License v3
Keywords: ocrfixr,spellcheck,OCR,contextual,BERT
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6, <3.9
Description-Content-Type: text/markdown
Requires-Dist: transformers
Requires-Dist: TextBlob
Requires-Dist: tensorflow (>=2.0)
Requires-Dist: importlib-resources
Requires-Dist: pandas

# OCRfixr

## OVERVIEW 
This project aims to automate the boring work of manually correcting OCR output from Distributed Proofreaders' book digitization projects


## CORRECTING 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 (TextBlob) | 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](https://pypi.org/project/OCRfixr/). 

```bash
pip install OCRfixr
```

By default, OCRfixr only returns the original string, with all changes incorporated:
```python
>>> 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:
```python
>>> spellcheck(text, return_fixes = "T").fix()
['The birds flew south', {'flevv': 'flew'}]
```

Use __full_results_by_paragraph__ for longer texts, to break out the text & associated changes by paragraph: 
```python
>>> text = "The birds flevv down\n south, but wefe quickly apprehended\n by border patrol agents"
>>> spellcheck(text, full_results_by_paragraph = "T").fix()
[['The birds flew down\n', {'flevv': 'flew'}],
 [' south, but were quickly apprehended\n', {'wefe': 'were'}],
 [' by border patrol agents', {}]]
```

Otherwise, the full text (+ any changes) will be returned in a single object:
```python
>>> text = "The birds flevv down\n south, but wefe quickly apprehended\n by border patrol agents"
>>> spellcheck(text, return_fixes = "T").fix()
['The birds flew down\n south, but were quickly apprehended\n by border patrol agents',
 {'flevv': 'flew', '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)_


### 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
__TextBlob__ powers spellcheck suggestions, and __transformers__ does the heavy lifting for BERT context modelling. SCOWL word list is Copyright 2000-2019 by Kevin Atkinson.
All book data comes from Distributed Proofreaders. Support them here: <https://www.pgdp.net/c/>

