This sample project helps you get started with the Adobe PDFServices Python SDK which currently has Extract PDF and Autotag PDF Operations.
The sample classes illustrate how to perform PDF-related extraction (extracting content of PDF in user friendly structured format) and autotagging (tagging content of PDF with rich structural information) using the SDK.
The sample application has the following requirements:
- Python : Version 3.6 or above. Python installation instructions can be found here.
The credentials file for the samples is pdfservices-api-credentials.json.
Before the samples can be run, set the environment variables PDF_SERVICES_CLIENT_ID and PDF_SERVICES_CLIENT_SECRET from the pdfservices-api-credentials.json file downloaded at the end of creation of credentials via Get Started workflow by running the following commands:
- For MacOS/Linux Users :
export PDF_SERVICES_CLIENT_ID=<YOUR CLIENT ID>
export PDF_SERVICES_CLIENT_SECRET=<YOUR CLIENT SECRET>
- For Windows Users :
SET PDF_SERVICES_CLIENT_ID=<YOUR CLIENT ID>
SET PDF_SERVICES_CLIENT_SECRET=<YOUR CLIENT SECRET>
The SDK also supports providing the authentication credentials at runtime, without storing them in a config file. Please refer this section to know more. Additionally, SDK can be configured to process the documents in the specified region. Please refer this section to know more.
Install the dependencies for the samples as listed in the requirements.txt file with this command:
pip install -r requirements.txt
The SDK uses the Python standard logging module. Customize the logging settings as needed.
Default Logging Config:
logging.getLogger(__name__).addHandler(logging.NullHandler())
The following sub-sections describe how to run the samples. Prior to running the samples, check that the credentials file is set up as described above and that the project has been built.
The code itself is in the extractpdf and autotag folders. Test files used by the samples can be found in resources/. When executed, all samples create an output
child folder under the project root directory to store their results.
The output of SDK extract operation is Zip package. The Zip package consists of following:
- The structuredData.json file with the extracted content & PDF element structure. See the JSON schema. Please refer the Styling JSON schema for a description of the output when the styling option is enabled.
- A renditions folder(s) containing renditions for each element type selected as input. The folder name is either “tables” or “figures” depending on your specified element type. Each folder contains renditions with filenames that correspond to the element information in the JSON file.
These samples illustrate how to extract PDF elements from PDF Document.
The sample class extract_txt_from_pdf.py extracts text elements from PDF Document.
python src/extractpdf/extract_txt_from_pdf.py
The sample class extract_txt_table_info_from_pdf.py extracts text, table elements from PDF Document.
python src/extractpdf/extract_txt_table_info_from_pdf.py
The sample class extract_txt_table_info_with_rendition_from_pdf.py extracts text, table elements along with table renditions
from PDF Document. Note that the output is a zip containing the structured information along with renditions as described
in section.
python src/extractpdf/extract_txt_table_info_with_rendition_from_pdf.py
The sample class extract_txt_table_info_with_figure_tables_rendition_from_pdf.py extracts text, table elements along with figure
and table element's renditions from PDF Document. Note that the output is a zip containing the structured information
along with renditions as described in section.
python src/extractpdf/extract_txt_table_info_with_figure_tables_rendition_from_pdf.py
The sample class extract_txt_from_pdf_with_custom_timeouts.py extracts text elements from PDF Document.
This sample highlights how to provide the custom value for connection timeout and socket timeout.
python src/extractpdf/extract_txt_from_pdf_with_custom_timeouts.py
The sample class extract_txt_from_pdf_with_in_memory_auth_credentials.py extracts text elements from PDF Document.
This sample highlights how to provide in-memory auth credentials for performing an operation.
This enables the clients to fetch the credentials from a secret server during runtime, instead of storing them in a file.
python src/extractpdf/extract_txt_from_pdf_with_in_memory_auth_credentials.py
The sample class extract_txt_with_char_bounds_from_pdf.py extracts text elements and bounding boxes for characters present in text blocks.
Note that the output is a zip containing the structured information
along with renditions as described in section.
python src/extractpdf/extract_txt_with_char_bounds_from_pdf.py
Extract Text, Table Elements and bounding boxes for Characters present in text blocks with Renditions of Table Elements
The sample class extract_txt_table_info_with_char_bounds_from_pdf.py extracts text, table elements, bounding boxes for characters present in text blocks and table element's renditions from PDF Document.
Note that the output is a zip containing the structured information
along with renditions as described in section.
python src/extractpdf/extract_txt_table_info_with_char_bounds_from_pdf.py
The sample class extract_txt_table_info_with_table_structure_from_pdf.py extracts text, table elements, table structures as CSV and table element's renditions from PDF Document.
Note that the output is a zip containing the structured information
along with renditions as described in section.
python src/extractpdf/extract_txt_table_info_with_table_structure_from_pdf.py
The sample class extract_txt_with_styling_info_from_pdf.py extracts text along with Styling Info.
Note that the output is a zip containing the structured information
along with renditions as described in section.
python src/extractpdf/extract_txt_with_styling_info_from_pdf.py
The sample class extract_txt_from_pdf_with_specified_region.py highlights how to configure the SDK to process the documents in the specified region.
python src/extractpdf/extract_txt_from_pdf_with_specified_region.py
These samples illustrate auto-tagging a PDF and generating the tagged PDF and an optional report which contains the information about the tags the tagged document contains.
The sample class autotag_pdf.py generates tagged PDF from a PDF.
python src/autotagpdf/autotag_pdf.py
The sample class autotag_pdf_with_options.py generates tagged PDF with a report and shift the headings in the output PDF file.
python src/autotagpdf/autotag_pdf_with_options.py
The sample class autotag_pdf_parameterised.py generates tagged PDF by setting options through command line arguments
Here is a sample list of command line arguments and their description:
--input < input file path >
--output < output file path >
--report { If this argument is present then the output will be generated with the report }
--shift_headings { If this argument is present then the headings will be shifted in the output PDF file }
python src/autotagpdf/autotag_pdf_parameterised.py --report --shift_headings --input resources/autotagPdfInput.pdf --output output/AutotagPDFParamaterised/
Contributions are welcome! Read the Contributing Guide for more information.
This project is licensed under the Apache2 License. See LICENSE for more information.