ocraccuracyreporter

============ Overview

Your OCR pipeline may have various stages and may use various tools. You need a simple way to run sample/s as a whole or piece by piece and have a way to say that the OCR accuracy is say 98%.

========= Usage

pip install ocraccuracyreporter from ocraccuracyreporter.oar import oar

.. topic:: initialising the reporter

oreport = oar(expected='john', given='joh', label='name')

print(oreport) name,john,joh,86,100,86,86,94,1

or you may have various ocr results for the same item, so you may want to initialise the expected alone with or without a label

oreport = oar(expected='john', label='name') oreport.given = 'joh' repr(oreoprt) if you are creating a csv report with header info label,expected,given,ratio,partial_ratio,token_sort_ratio,token_set_ratio,jaro_winkler,distance name,john,joh,86,100,86,86,94,1

.. topic:: Items in the report

ratio - uses pure Levenshtein Distance based matching (100 - means perfect match)

partial_ratio - matches based on best substrings

token_sort_ratio - tokenizes the strings and sorts them alphabetically

token_set_ratio - tokenizes the strings and compared the intersection

jaro_winkler - this algorithm giving more weight to common prefix (for example, some parts are good, missing others)

distance - this shows how many characters are really different in given compared to expected

========= Class variables

label - a meaningful name for the ocr string. expected - expected result given - result you got out of ocr pipeline

total_expected_char_count - calculated expected char count total_expected_word_count - calculated expected word count

total_given_char_count - calculated given char count total_given_word_count - calculated given word count