ABBYY FineReader Engine provides two options:
This object represents a single hypothesis for a word and contains the text of the hypothesis, type of model, the average width of stroke, and information on whether the hypothesis has been found in the dictionary.
This object represents a single hypothesis for a character and contains character confidence, probability that a character is written with a serif font, and information on whether the character is superscript or subscript.
Example of Character Recognition
During the layout analysis step, the text areas, lines and single characters coordinates are detected. After the character separation, each character is recognized with different text recognition technologies/algorithms/classifiers.
The recognition confidence of a single character image is a numerical estimate of the probability that the image does in fact represent this character.
For example, an image of the letter “e” may be recognized as
the letter “e” with a confidence of 95
the letter “c” with a confidence of 85,
the letter “o” with a confidence of 65, etc.
The hypothesis with the highest confidence rating is selected as the recognition result. However, the selection also depends on the context (i.e. the word in which the character occurs) and the results of a differential comparison.
If the word with the “e” hypothesis is not a dictionary word while the word with the “c” hypothesis is a dictionary word, the latter will be selected as the recognition result, even though its confidence rating will still be 85. The remining the recognition variants can be obtained as hypotheses.
Important Note: The Voting API is only available for OCR, not for for recognition of hand-printed texts.