mirror of
https://github.com/mikegerber/ocrd_calamari.git
synced 2025-06-09 03:39:55 +02:00
improve process() docstring
This commit is contained in:
parent
3bde7cb37f
commit
4c6d6655e1
1 changed files with 25 additions and 1 deletions
|
@ -67,7 +67,31 @@ class CalamariRecognize(Processor):
|
|||
|
||||
def process(self):
|
||||
"""
|
||||
Performs the recognition.
|
||||
Perform text recognition with Calamari on the workspace.
|
||||
|
||||
For each page of the input file group, open and deserialize input PAGE-XML
|
||||
and its respective images. Then iterate over the element hierarchy down to
|
||||
the line level.
|
||||
|
||||
For each textline, retrieve a segment image according to the layout annotation
|
||||
(from an existing ``AlternativeImage``, or by cropping into the higher-level
|
||||
images, and deskewing when applicable).
|
||||
|
||||
If the line element contained any previous text results or word segmentation,
|
||||
delete it.
|
||||
|
||||
Convert the line image to a Numpy array and pass it to the recognizer. Aggregate
|
||||
character results on the line level, stripping leading and trailing white space,
|
||||
and selecting the best hypothesis for each position. Annotate the resulting
|
||||
TextEquiv string and (average) confidence on the line segment.
|
||||
|
||||
If ``texequiv_level`` is ``word`` or ``glyph``, then additionally create word
|
||||
level segments by splitting at white space characters, using the vertical
|
||||
line coordinates and horizontal white space boundaries. In the case of ``glyph``,
|
||||
create glyph level segments as well, adding all alternative character hypotheses
|
||||
down to ``glyph_conf_cutoff`` confidence threshold.
|
||||
|
||||
Produce a new PAGE output file by serialising the resulting hierarchy.
|
||||
"""
|
||||
log = getLogger('processor.CalamariRecognize')
|
||||
|
||||
|
|
Loading…
Add table
Add a link
Reference in a new issue