I am attempting to use the now supported PDF/TIFF Document Text Detection from the Google Cloud Vision API. Using their example code I am able to submit a PDF and receive back a JSON object with the extracted text. My issue is that the JSON file that is saved to GCS only contains bounding boxes and text for "symbols", i.e. each character in each word. This makes the JSON object quite unwieldy and very difficult to use. I'd like to be able to get the text and bounding boxes for "LINES", "PARAGRAPHS" and "BLOCKS", but I can't seem to find a way to do it via the AsyncAnnotateFileRequest()
method.
The sample code is as follows:
def async_detect_document(gcs_source_uri, gcs_destination_uri):
"""OCR with PDF/TIFF as source files on GCS"""
# Supported mime_types are: 'application/pdf' and 'image/tiff'
mime_type = 'application/pdf'
# How many pages should be grouped into each json output file.
batch_size = 2
client = vision.ImageAnnotatorClient()
feature = vision.types.Feature(
type=vision.enums.Feature.Type.DOCUMENT_TEXT_DETECTION)
gcs_source = vision.types.GcsSource(uri=gcs_source_uri)
input_config = vision.types.InputConfig(
gcs_source=gcs_source, mime_type=mime_type)
gcs_destination = vision.types.GcsDestination(uri=gcs_destination_uri)
output_config = vision.types.OutputConfig(
gcs_destination=gcs_destination, batch_size=batch_size)
async_request = vision.types.AsyncAnnotateFileRequest(
features=[feature], input_config=input_config,
output_config=output_config)
operation = client.async_batch_annotate_files(
requests=[async_request])
print('Waiting for the operation to finish.')
operation.result(timeout=180)
# Once the request has completed and the output has been
# written to GCS, we can list all the output files.
storage_client = storage.Client()
match = re.match(r'gs://([^/]+)/(.+)', gcs_destination_uri)
bucket_name = match.group(1)
prefix = match.group(2)
bucket = storage_client.get_bucket(bucket_name=bucket_name)
# List objects with the given prefix.
blob_list = list(bucket.list_blobs(prefix=prefix))
print('Output files:')
for blob in blob_list:
print(blob.name)
# Process the first output file from GCS.
# Since we specified batch_size=2, the first response contains
# the first two pages of the input file.
output = blob_list[0]
json_string = output.download_as_string()
response = json_format.Parse(
json_string, vision.types.AnnotateFileResponse())
# The actual response for the first page of the input file.
first_page_response = response.responses[0]
annotation = first_page_response.full_text_annotation
# Here we print the full text from the first page.
# The response contains more information:
# annotation/pages/blocks/paragraphs/words/symbols
# including confidence scores and bounding boxes
print(u'Full text:\n{}'.format(
annotation.text))
Unfortunately when using the DOCUMENT_TEXT_DETECTION
type, you can only get the full text per-page, or the individual symbols. It's not too difficult to put together the paragraphs and lines from the symbols though, something like this should work (extending from your example):
breaks = vision.enums.TextAnnotation.DetectedBreak.BreakType
paragraphs = []
lines = []
for page in annotation.pages:
for block in page.blocks:
for paragraph in block.paragraphs:
para = ""
line = ""
for word in paragraph.words:
for symbol in word.symbols:
line += symbol.text
if symbol.property.detected_break.type == breaks.SPACE:
line += ' '
if symbol.property.detected_break.type == breaks.EOL_SURE_SPACE:
line += ' '
lines.append(line)
para += line
line = ''
if symbol.property.detected_break.type == breaks.LINE_BREAK:
lines.append(line)
para += line
line = ''
paragraphs.append(para)
print(paragraphs)
print(lines)