def ocr_kraken(doc, method=u'ocr_kraken', model=None):
"""
Runs kraken on an input document and writes a TEI file.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string append to all output files
model (unicode): Identifier for the font model to use
Returns:
(unicode, unicode): Storage tuple for the output file
"""
input_path = storage.get_abs_path(*doc[1])
output_path = (doc[1][0], os.path.splitext(storage.insert_suffix(doc[1][1],
method,
model))[0]
+ '.xml')
logger.debug('Searching for model {}'.format(model))
if model in nidaba_cfg['kraken_models']:
model = storage.get_abs_path(*(nidaba_cfg['kraken_models'][model]))
elif model in nidaba_cfg['ocropus_models']:
model = storage.get_abs_path(*(nidaba_cfg['ocropus_models'][model]))
else:
raise NidabaInvalidParameterException('Model not defined in '
'configuration')
img = Image.open(input_path)
logger.debug('Reading TEI segmentation from {}'.format(doc[1]))
tei = OCRRecord()
with storage.StorageFile(*doc[0]) as seg:
tei.load_tei(seg)
logger.debug('Clearing out word/grapheme boxes')
# kraken is a line recognizer
tei.clear_graphemes()
tei.clear_segments()
# add and scope new responsibility statement
tei.add_respstmt('kraken', 'character recognition')
lines = tei.lines
logger.debug('Loading model {}'.format(model))
rnn = models.load_any(model)
i = 0
logger.debug('Start recognizing characters')
for line_id, rec in zip(lines, rpred.rpred(rnn, img, [x['bbox'] for x in lines.itervalues()])):
# scope the current line and add all graphemes recognized by kraken to
# it.
logger.debug('Scoping line {}'.format(line_id))
tei.scope_line(line_id)
i += 1
splits = regex.split(u'(\s+)', rec.prediction)
line_offset = 0
for segment, whitespace in izip_longest(splits[0::2], splits[1::2]):
if len(segment):
seg_bbox = max_bbox(rec.cuts[line_offset:line_offset + len(segment)])
logger.debug('Creating new segment at {} {} {} {}'.format(*seg_bbox))
tei.add_segment(seg_bbox)
logger.debug('Adding graphemes (segment): {}'.format(rec.prediction[line_offset:line_offset+len(segment)]))
tei.add_graphemes([{'grapheme': x[0],
'bbox': x[1],
'confidence': int(x[2] * 100)} for x in rec[line_offset:line_offset+len(segment)]])
line_offset += len(segment)
if whitespace:
logger.debug('Adding graphemes (whitespace): {}'.format(rec.prediction[line_offset:line_offset+len(whitespace)]))
seg_bbox = max_bbox(rec.cuts[line_offset:line_offset + len(whitespace)])
tei.add_segment(seg_bbox)
tei.add_graphemes([{'grapheme': x[0],
'bbox': x[1],
'confidence': int(x[2] * 100)} for x in rec[line_offset:line_offset+len(whitespace)]])
line_offset += len(whitespace)
with storage.StorageFile(*output_path, mode='wb') as fp:
logger.debug('Writing TEI to {}'.format(fp.abs_path))
tei.write_tei(fp)
return output_path
def ocr_kraken(doc, method=u'ocr_kraken', model=None):
"""
Runs kraken on an input document and writes a TEI file.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string append to all output files
model (unicode): Identifier for the font model to use
Returns:
(unicode, unicode): Storage tuple for the output file
"""
output_path = (
doc[0],
os.path.splitext(storage.insert_suffix(doc[1], method, model))[0] +
'.xml')
logger.debug('Loading model {}'.format(model))
try:
rnn = models.load_any(mod_db[model])
except Exception as e:
raise NidabaInvalidParameterException(str(e))
logger.debug('Reading TEI segmentation from {}'.format(doc))
tei = OCRRecord()
with storage.StorageFile(*doc) as seg:
tei.load_tei(seg)
img = Image.open(
storage.get_abs_path(*storage.get_storage_path_url(tei.img)))
if is_bitonal(img):
img = img.convert('1')
else:
raise NidabaInvalidParameterException('Input image is not bitonal')
logger.debug('Clearing out word/grapheme boxes')
# kraken is a line recognizer
tei.clear_graphemes()
tei.clear_segments()
# add and scope new responsibility statement
tei.add_respstmt('kraken', 'character recognition')
lines = tei.lines
i = 0
rnn = models.load_any(mod_db[model])
logger.debug('Start recognizing characters')
for line_id, rec in izip(
lines,
rpred.rpred(
rnn, img, {
'text_direction': 'horizontal-tb',
'boxes': [list(x['bbox']) for x in lines.itervalues()]
})):
# scope the current line and add all graphemes recognized by kraken to
# it.
logger.debug('Scoping line {}'.format(line_id))
tei.scope_line(line_id)
i += 1
splits = regex.split(u'(\s+)', rec.prediction)
line_offset = 0
for segment, whitespace in izip_longest(splits[0::2], splits[1::2]):
if len(segment):
seg_bbox = max_bbox(rec.cuts[line_offset:line_offset +
len(segment)])
logger.debug(
'Creating new segment at {} {} {} {}'.format(*seg_bbox))
tei.add_segment(seg_bbox)
logger.debug('Adding graphemes (segment): {}'.format(
rec.prediction[line_offset:line_offset + len(segment)]))
tei.add_graphemes([{
'grapheme': x[0],
'bbox': x[1],
'confidence': int(x[2] * 100)
} for x in rec[line_offset:line_offset + len(segment)]])
line_offset += len(segment)
if whitespace:
logger.debug('Adding graphemes (whitespace): {}'.format(
rec.prediction[line_offset:line_offset + len(whitespace)]))
seg_bbox = max_bbox(rec.cuts[line_offset:line_offset +
len(whitespace)])
tei.add_segment(seg_bbox)
tei.add_graphemes([{
'grapheme': x[0],
'bbox': x[1],
'confidence': int(x[2] * 100)
} for x in rec[line_offset:line_offset + len(whitespace)]])
line_offset += len(whitespace)
with storage.StorageFile(*output_path, mode='wb') as fp:
logger.debug('Writing TEI to {}'.format(fp.abs_path))
tei.write_tei(fp)
return output_path
def ocr(image_path, segmentation_path, output_path, model_path):
"""
Scan a single image with ocropus.
Reads a single image file from ```imagepath``` and writes the recognized
text as a TEI document into output_path.
Args:
image_path (unicode): Path of the input file
segmentation_path (unicode): Path of the segmentation XML file.
output_path (unicode): Path of the output file
model_path (unicode): Path of the recognition model. Must be a pyrnn.gz
pickle dump interoperable with ocropus-rpred.
Returns:
(unicode): A string of the output file that is actually written. As
Ocropus rewrites output file paths without notice it may be
different from the ```outputfilepath``` argument.
Raises:
NidabaOcropusException: Ocropus somehow failed. The error output is
contained in the message but as it is de facto
unusable as a library it's impossible to deduct
the nature of the problem.
"""
try:
logger.debug('Loading pyrnn from {}'.format(model_path))
network = ocrolib.load_object(model_path, verbose=0)
lnorm = getattr(network, "lnorm")
except Exception as e:
raise NidabaOcropusException('Something somewhere broke: ' + e.msg)
im = Image.open(image_path)
logger.debug('Loading TEI segmentation {}'.format(segmentation_path))
tei = OCRRecord()
with open(segmentation_path, 'r') as seg_fp:
tei.load_tei(seg_fp)
logger.debug('Clearing out word/grapheme boxes')
# ocropus is a line recognizer
tei.clear_graphemes()
tei.clear_segments()
# add and scope new responsibility statement
tei.add_respstmt('ocropus', 'character recognition')
for line_id, box in tei.lines.iteritems():
logger.debug('Recognizing line {}'.format(box['bbox']))
line = ocrolib.pil2array(im.crop(box['bbox']))
temp = np.amax(line) - line
temp = temp * 1.0 / np.amax(temp)
lnorm.measure(temp)
line = lnorm.normalize(line, cval=np.amax(line))
if line.ndim == 3:
np.mean(line, 2)
line = ocrolib.lstm.prepare_line(line, 16)
pred = network.predictString(line)
pred = ocrolib.normalize_text(pred)
logger.debug('Scoping line {}'.format(line_id))
tei.scope_line(line_id)
logger.debug('Adding graphemes: {}'.format(pred))
tei.add_graphemes({'grapheme': x} for x in pred)
with open(output_path, 'wb') as fp:
logger.debug('Writing TEI to {}'.format(fp.name))
tei.write_tei(fp)
return output_path
