qurator-spk/ocrd_calamari
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🚧 Use character positions as word segmentation

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Gerber, Mike 2020-01-31 17:45:00 +01:00
parent 17dbeb2480
commit 24532f693a
1 changed files with 32 additions and 4 deletions
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36
ocrd_calamari/recognize.py
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@ -13,9 +13,10 @@ from ocrd_models.ocrd_page import (
LabelType, LabelsType, LabelType, LabelsType,
MetadataItemType, MetadataItemType,
TextEquivType, TextEquivType,
WordType, CoordsType,
to_xml to_xml
) )
from ocrd_utils import getLogger, concat_padded, MIMETYPE_PAGE from ocrd_utils import getLogger, concat_padded, coordinates_for_segment, points_from_polygon, MIMETYPE_PAGE
from ocrd_calamari.config import OCRD_TOOL, TF_CPP_MIN_LOG_LEVEL from ocrd_calamari.config import OCRD_TOOL, TF_CPP_MIN_LOG_LEVEL
@ -69,7 +70,7 @@ class CalamariRecognize(Processor):
for (line_no, line) in enumerate(textlines): for (line_no, line) in enumerate(textlines):
log.debug("Recognizing line '%s' in region '%s'", line_no, region.id) log.debug("Recognizing line '%s' in region '%s'", line_no, region.id)
line_image, line_xywh = self.workspace.image_from_segment(line, region_image, region_xywh) line_image, line_coords = self.workspace.image_from_segment(line, region_image, region_xywh)
line_image_np = np.array(line_image, dtype=np.uint8) line_image_np = np.array(line_image, dtype=np.uint8)
raw_results = list(self.predictor.predict_raw([line_image_np], progress_bar=False))[0] raw_results = list(self.predictor.predict_raw([line_image_np], progress_bar=False))[0]
@ -82,14 +83,41 @@ class CalamariRecognize(Processor):
line_text = prediction.sentence line_text = prediction.sentence
line_conf = prediction.avg_char_probability line_conf = prediction.avg_char_probability
# Delete existing results
if line.get_TextEquiv(): if line.get_TextEquiv():
log.warning("Line '%s' already contained text results", line.id) log.warning("Line '%s' already contained text results", line.id)
line.set_TextEquiv([TextEquivType(Unicode=line_text, conf=line_conf)]) line.set_TextEquiv([])
if line.get_Word(): if line.get_Word():
log.warning("Line '%s' already contained word segmentation", line.id) log.warning("Line '%s' already contained word segmentation", line.id)
line.set_Word([]) line.set_Word([])
# Save line results
line.set_TextEquiv([TextEquivType(Unicode=line_text, conf=line_conf)])
# Save word results
# XXX For early development just put every char = glyph into its own word
for word_no, p in enumerate(prediction.positions):
start = p.global_start
end = p.global_end
# XXX Maybe use version in ocrd_tesserocr
h = line_image.height
polygon = [(start, 0), (end, 0), (end, h), (start, h)]
points = points_from_polygon(coordinates_for_segment(polygon, None, line_coords))
word = WordType(
id='%s_word%04d' % (line.id, word_no),
Coords=CoordsType(points))
chars = sorted(p.chars, key=lambda k: k.probability, reverse=True)
for index, char in enumerate(chars):
if char.char:
word.add_TextEquiv(TextEquivType(Unicode=char.char, index=index, conf=char.probability))
# XXX Note that omission probabilities are not normalized?!
line.add_Word(word)
_page_update_higher_textequiv_levels('line', pcgts) _page_update_higher_textequiv_levels('line', pcgts)