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        "description": "============\nOverview\n============\n\nYour OCR pipeline may have various stages and may use various tools.\nYou 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%.\n\n=========\nUsage\n=========\n\n>>> pip install ocraccuracyreporter\n>>> from ocraccuracyreporter.oar import oar\n\n.. topic:: initialising the reporter\n\n>>> oreport = oar(expected='john', given='joh', label='name')\n\n>>> print(oreport)\n>>> name,john,joh,86,100,86,86,94,1\n\nor you may have various ocr results for the same item, so you may want to initialise the expected alone\nwith or without a label\n\n>>> oreport = oar(expected='john', label='name')\n>>> oreport.given = 'joh'\n>>> repr(oreoprt)\nif you are creating a csv report with header info\n>>>label,expected,given,ratio,partial_ratio,token_sort_ratio,token_set_ratio,jaro_winkler,distance\n  name,john,joh,86,100,86,86,94,1\n\n  .. topic:: Items in the report\n\n\n  ratio - uses pure Levenshtein Distance based matching\n          (100 - means perfect match)\n\n  partial_ratio - matches based on best substrings\n\n  token_sort_ratio - tokenizes the strings and sorts them alphabetically\n\n  token_set_ratio - tokenizes the strings and compared the intersection\n\n  jaro_winkler - this algorithm giving more weight to common prefix\n                 (for example, some parts are good, missing others)\n\n  distance - this shows how many characters are really different in given\n             compared to expected\n\n\n\n\n=========\nClass variables\n=========\n\nlabel  - a meaningful name for the ocr string.\nexpected - expected result\ngiven - result you got out of ocr pipeline\n\ntotal_expected_char_count - calculated expected char count\ntotal_expected_word_count - calculated expected word count\n\ntotal_given_char_count - calculated given char count\ntotal_given_word_count - calculated given word count\n",
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