ניתוח טפסים באמצעות Document AI (Python)

pip3 install --upgrade pandas 

gsutil cp gs://cloud-samples-data/documentai/codelabs/form-parser/intake-form.pdf . 

ls -ltr intake-form.pdf 

import pandas as pd from google.cloud import documentai_v1 as documentai   def online_process(     project_id: str,     location: str,     processor_id: str,     file_path: str,     mime_type: str, ) -> documentai.Document:     """     Processes a document using the Document AI Online Processing API.     """      opts = {"api_endpoint": f"{location}-documentai.googleapis.com"}      # Instantiates a client     documentai_client = documentai.DocumentProcessorServiceClient(client_options=opts)      # The full resource name of the processor, e.g.:     # projects/project-id/locations/location/processor/processor-id     # You must create new processors in the Cloud Console first     resource_name = documentai_client.processor_path(project_id, location, processor_id)      # Read the file into memory     with open(file_path, "rb") as image:         image_content = image.read()          # Load Binary Data into Document AI RawDocument Object         raw_document = documentai.RawDocument(             content=image_content, mime_type=mime_type         )          # Configure the process request         request = documentai.ProcessRequest(             name=resource_name, raw_document=raw_document         )          # Use the Document AI client to process the sample form         result = documentai_client.process_document(request=request)          return result.document   def trim_text(text: str):     """     Remove extra space characters from text (blank, newline, tab, etc.)     """     return text.strip().replace("\n", " ")   PROJECT_ID = "YOUR_PROJECT_ID" LOCATION = "YOUR_PROJECT_LOCATION"  # Format is 'us' or 'eu' PROCESSOR_ID = "FORM_PARSER_ID"  # Create processor in Cloud Console  # The local file in your current working directory FILE_PATH = "intake-form.pdf" # Refer to https://cloud.google.com/document-ai/docs/processors-list # for supported file types MIME_TYPE = "application/pdf"  document = online_process(     project_id=PROJECT_ID,     location=LOCATION,     processor_id=PROCESSOR_ID,     file_path=FILE_PATH,     mime_type=MIME_TYPE, )  names = [] name_confidence = [] values = [] value_confidence = []  for page in document.pages:     for field in page.form_fields:         # Get the extracted field names         names.append(trim_text(field.field_name.text_anchor.content))         # Confidence - How "sure" the Model is that the text is correct         name_confidence.append(field.field_name.confidence)          values.append(trim_text(field.field_value.text_anchor.content))         value_confidence.append(field.field_value.confidence)  # Create a Pandas Dataframe to print the values in tabular format. df = pd.DataFrame(     {         "Field Name": names,         "Field Name Confidence": name_confidence,         "Field Value": values,         "Field Value Confidence": value_confidence,     } )  print(df) 

$ python3 form_parser.py                                            Field Name  Field Name Confidence                                        Field Value  Field Value Confidence 0                                            Phone #:               0.999982                                     (906) 917-3486                0.999982 1                                  Emergency Contact:               0.999972                                         Eva Walker                0.999972 2                                     Marital Status:               0.999951                                             Single                0.999951 3                                             Gender:               0.999933                                                  F                0.999933 4                                         Occupation:               0.999914                                  Software Engineer                0.999914 5                                        Referred By:               0.999862                                               None                0.999862 6                                               Date:               0.999858                                            9/14/19                0.999858 7                                                DOB:               0.999716                                         09/04/1986                0.999716 8                                            Address:               0.999147                                     24 Barney Lane                0.999147 9                                               City:               0.997718                                             Towaco                0.997718 10                                              Name:               0.997345                                       Sally Walker                0.997345 11                                             State:               0.996944                                                 NJ                0.996944 ... 

gsutil cp gs://cloud-samples-data/documentai/codelabs/form-parser/form_with_tables.pdf . 

ls -ltr form_with_tables.pdf 

# type: ignore[1] """ Uses Document AI online processing to call a form parser processor Extracts the tables and data in the document. """ from os.path import splitext from typing import List, Sequence  import pandas as pd from google.cloud import documentai   def online_process(     project_id: str,     location: str,     processor_id: str,     file_path: str,     mime_type: str, ) -> documentai.Document:     """     Processes a document using the Document AI Online Processing API.     """      opts = {"api_endpoint": f"{location}-documentai.googleapis.com"}      # Instantiates a client     documentai_client = documentai.DocumentProcessorServiceClient(client_options=opts)      # The full resource name of the processor, e.g.:     # projects/project-id/locations/location/processor/processor-id     # You must create new processors in the Cloud Console first     resource_name = documentai_client.processor_path(project_id, location, processor_id)      # Read the file into memory     with open(file_path, "rb") as image:         image_content = image.read()          # Load Binary Data into Document AI RawDocument Object         raw_document = documentai.RawDocument(             content=image_content, mime_type=mime_type         )          # Configure the process request         request = documentai.ProcessRequest(             name=resource_name, raw_document=raw_document         )          # Use the Document AI client to process the sample form         result = documentai_client.process_document(request=request)          return result.document   def get_table_data(     rows: Sequence[documentai.Document.Page.Table.TableRow], text: str ) -> List[List[str]]:     """     Get Text data from table rows     """     all_values: List[List[str]] = []     for row in rows:         current_row_values: List[str] = []         for cell in row.cells:             current_row_values.append(                 text_anchor_to_text(cell.layout.text_anchor, text)             )         all_values.append(current_row_values)     return all_values   def text_anchor_to_text(text_anchor: documentai.Document.TextAnchor, text: str) -> str:     """     Document AI identifies table data by their offsets in the entirety of the     document's text. This function converts offsets to a string.     """     response = ""     # If a text segment spans several lines, it will     # be stored in different text segments.     for segment in text_anchor.text_segments:         start_index = int(segment.start_index)         end_index = int(segment.end_index)         response += text[start_index:end_index]     return response.strip().replace("\n", " ")   PROJECT_ID = "YOUR_PROJECT_ID" LOCATION = "YOUR_PROJECT_LOCATION"  # Format is 'us' or 'eu' PROCESSOR_ID = "FORM_PARSER_ID"  # Create processor before running sample  # The local file in your current working directory FILE_PATH = "form_with_tables.pdf" # Refer to https://cloud.google.com/document-ai/docs/file-types # for supported file types MIME_TYPE = "application/pdf"  document = online_process(     project_id=PROJECT_ID,     location=LOCATION,     processor_id=PROCESSOR_ID,     file_path=FILE_PATH,     mime_type=MIME_TYPE, )  header_row_values: List[List[str]] = [] body_row_values: List[List[str]] = []  # Input Filename without extension output_file_prefix = splitext(FILE_PATH)[0]  for page in document.pages:     for index, table in enumerate(page.tables):         header_row_values = get_table_data(table.header_rows, document.text)         body_row_values = get_table_data(table.body_rows, document.text)          # Create a Pandas Dataframe to print the values in tabular format.         df = pd.DataFrame(             data=body_row_values,             columns=pd.MultiIndex.from_arrays(header_row_values),         )          print(f"Page {page.page_number} - Table {index}")         print(df)          # Save each table as a CSV file         output_filename = f"{output_file_prefix}_pg{page.page_number}_tb{index}.csv"         df.to_csv(output_filename, index=False) 

$ python3 table_parsing.py Page 1 - Table 0      Item    Description 0  Item 1  Description 1 1  Item 2  Description 2 2  Item 3  Description 3 Page 1 - Table 1   Form Number:     12345678 0   Form Date:   2020/10/01 1        Name:   First Last 2     Address:  123 Fake St 

$ ls form_with_tables_pg1_tb0.csv form_with_tables_pg1_tb1.csv table_parsing.py