def do_document_ocr(queue_document):
"""
Try first to extract text from document pages using the registered
parser, if the parser fails or if there is no parser registered for
the document mimetype do a visual OCR by calling tesseract
"""
for document_page in queue_document.document.pages.all():
try:
# Try to extract text by means of a parser
parse_document_page(document_page)
except (ParserError, ParserUnknownFile):
# Fall back to doing visual OCR
ocr_transformations, warnings = queue_document.get_transformation_list(
)
document_filepath = document_page.document.get_image_cache_name(
page=document_page.page_number)
unpaper_output_filename = u'%s_unpaper_out_page_%s%s%s' % (
document_page.document.uuid, document_page.page_number,
os.extsep, UNPAPER_FILE_FORMAT)
unpaper_output_filepath = os.path.join(TEMPORARY_DIRECTORY,
unpaper_output_filename)
unpaper_input = convert(document_filepath,
file_format=UNPAPER_FILE_FORMAT,
transformations=ocr_transformations)
execute_unpaper(input_filepath=unpaper_input,
output_filepath=unpaper_output_filepath)
#from PIL import Image, ImageOps
#im = Image.open(document_filepath)
##if im.mode=='RGBA':
## im=im.convert('RGB')
##im = im.convert('L')
#im = ImageOps.grayscale(im)
#im.save(unpaper_output_filepath)
# Convert to TIFF
pre_ocr_filepath = convert(input_filepath=unpaper_output_filepath,
file_format=DEFAULT_OCR_FILE_FORMAT)
# Tesseract needs an explicit file extension
pre_ocr_filepath_w_ext = os.extsep.join(
[pre_ocr_filepath, DEFAULT_OCR_FILE_EXTENSION])
os.rename(pre_ocr_filepath, pre_ocr_filepath_w_ext)
try:
ocr_text = run_tesseract(pre_ocr_filepath_w_ext,
TESSERACT_LANGUAGE)
document_page.content = ocr_cleanup(ocr_text)
document_page.page_label = _(u'Text from OCR')
document_page.save()
finally:
cleanup(pre_ocr_filepath_w_ext)
cleanup(unpaper_input)
cleanup(document_filepath)
cleanup(unpaper_output_filepath)
def do_document_ocr(queue_document):
"""
Try first to extract text from document pages using the registered
parser, if the parser fails or if there is no parser registered for
the document mimetype do a visual OCR by calling tesseract
"""
for document_page in queue_document.document.documentpage_set.all():
try:
# Try to extract text by means of a parser
parse_document_page(document_page)
except (ParserError, ParserUnknownFile):
# Fall back to doing visual OCR
ocr_transformations, warnings = queue_document.get_transformation_list()
document_filepath = document_page.document.get_image_cache_name(page=document_page.page_number)
unpaper_output_filename = u"%s_unpaper_out_page_%s%s%s" % (
document_page.document.uuid,
document_page.page_number,
os.extsep,
UNPAPER_FILE_FORMAT,
)
unpaper_output_filepath = os.path.join(TEMPORARY_DIRECTORY, unpaper_output_filename)
unpaper_input = convert(
document_filepath, file_format=UNPAPER_FILE_FORMAT, transformations=ocr_transformations
)
execute_unpaper(input_filepath=unpaper_input, output_filepath=unpaper_output_filepath)
# from PIL import Image, ImageOps
# im = Image.open(document_filepath)
##if im.mode=='RGBA':
## im=im.convert('RGB')
##im = im.convert('L')
# im = ImageOps.grayscale(im)
# im.save(unpaper_output_filepath)
# Convert to TIFF
pre_ocr_filepath = convert(input_filepath=unpaper_output_filepath, file_format=DEFAULT_OCR_FILE_FORMAT)
# Tesseract needs an explicit file extension
pre_ocr_filepath_w_ext = os.extsep.join([pre_ocr_filepath, DEFAULT_OCR_FILE_EXTENSION])
os.rename(pre_ocr_filepath, pre_ocr_filepath_w_ext)
try:
ocr_text = run_tesseract(pre_ocr_filepath_w_ext, TESSERACT_LANGUAGE)
document_page.content = ocr_cleanup(ocr_text)
document_page.page_label = _(u"Text from OCR")
document_page.save()
finally:
cleanup(pre_ocr_filepath_w_ext)
cleanup(unpaper_input)
cleanup(document_filepath)
cleanup(unpaper_output_filepath)
def setUp(self):
from ocr.parsers import parse_document_page
self.document_type = DocumentType(name='test doc type')
self.document_type.save()
self.document = Document(
document_type=self.document_type,
description='description',
)
self.document.save()
file_object = open(os.path.join(settings.PROJECT_ROOT, 'contrib', 'mayan_11_1.pdf'))
new_version = self.document.new_version(file=File(file_object, name='mayan_11_1.pdf'))
file_object.close()
# Text extraction on the first page only
parse_document_page(self.document.latest_version.pages.all()[0])
def setUp(self):
from ocr.parsers import parse_document_page
self.document_type = DocumentType(name='test doc type')
self.document_type.save()
self.document = Document(
document_type=self.document_type,
description='description',
)
self.document.save()
with open(TEST_DOCUMENT_PATH) as file_object:
new_version = self.document.new_version(file=File(file_object, name='mayan_11_1.pdf'))
# Text extraction on the first page only
parse_document_page(self.document.latest_version.pages.all()[0])
