itext7.pdf2data 2.1.7

pdf2Data is an iText 7 add-on that allows you to extract and process data locked inside your PDF invoices

There is a newer version of this package available.
See the version list below for details.
Install-Package itext7.pdf2data -Version 2.1.7
dotnet add package itext7.pdf2data --version 2.1.7
<PackageReference Include="itext7.pdf2data" Version="2.1.7" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add itext7.pdf2data --version 2.1.7
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

pdf2Data is an iText 7 add-on that allows you to easily extract data from PDF documents.
It offers a framework to recognize data inside PDF documents, based on selection rules that you define in a template.

With pdf2Data you can automate the process of extracting data in a secure way.

  • Automate PDF data extraction from PDF invoices, forms and other documents: extract and process data from small or large volumes of PDFs by defining the information that is important for your data processes in a template. Automate PDF data extraction with programming .NET (C#).

  • Define which specific data you want to target for PDF data extraction: easily define the desired information you want to extract in a template with the pdf2Data template editor. pdf2Data for PDF data extraction works with all PDF documents, such as invoices, forms, reports etc. and makes PDF data processing a highly efficient part of your workflow.

  • Integrate automated PDF data extraction into your existing document process: pdf2Data uses open standards to facilitate integration, which makes integrating it into existing workflows easy and fast. It includes SDKs for amongst others .NET (C#) as well as a command line interface. PDF data processing for the 21st century.

Capabilities:

pdf2Data works by defining the areas, fonts, patterns, or tables of interest in a template that is used for all PDFs created in the same format, such as an invoice or other commercial documents.
You then can define areas of interest with selectors.

Each selector uses a different way of identifying the information that is important and can be used in conjunction or alone to meet your needs.

  • Extract data from PDF documents: leverage iText 7 Core content extraction, for a high-fidelity recognition process of text and images for PDF data processing.
  • Intuitive extraction configuration: pdf2Data has comprehensive out of the box functionality, with the flexibility to extend and customize. Focus on easy integration and open standards.
  • Use templates to streamline extraction: define areas of interest and selection rules to get exactly the content you need.
  • Integrate in your PDF and/or data workflow: data is output in a structured, reusable format for further processing, with access to the page coordinates of the extracted content.

Visit our knowledge base to find code samples, manuals, documentation and more.

You can also find its API here.

Try our code in our developer sandbox or use our free apps, all in our iText 7 Demo Lab.

pdf2Data is an iText 7 add-on that allows you to easily extract data from PDF documents.
It offers a framework to recognize data inside PDF documents, based on selection rules that you define in a template.

With pdf2Data you can automate the process of extracting data in a secure way.

  • Automate PDF data extraction from PDF invoices, forms and other documents: extract and process data from small or large volumes of PDFs by defining the information that is important for your data processes in a template. Automate PDF data extraction with programming .NET (C#).

  • Define which specific data you want to target for PDF data extraction: easily define the desired information you want to extract in a template with the pdf2Data template editor. pdf2Data for PDF data extraction works with all PDF documents, such as invoices, forms, reports etc. and makes PDF data processing a highly efficient part of your workflow.

  • Integrate automated PDF data extraction into your existing document process: pdf2Data uses open standards to facilitate integration, which makes integrating it into existing workflows easy and fast. It includes SDKs for amongst others .NET (C#) as well as a command line interface. PDF data processing for the 21st century.

Capabilities:

pdf2Data works by defining the areas, fonts, patterns, or tables of interest in a template that is used for all PDFs created in the same format, such as an invoice or other commercial documents.
You then can define areas of interest with selectors.

Each selector uses a different way of identifying the information that is important and can be used in conjunction or alone to meet your needs.

  • Extract data from PDF documents: leverage iText 7 Core content extraction, for a high-fidelity recognition process of text and images for PDF data processing.
  • Intuitive extraction configuration: pdf2Data has comprehensive out of the box functionality, with the flexibility to extend and customize. Focus on easy integration and open standards.
  • Use templates to streamline extraction: define areas of interest and selection rules to get exactly the content you need.
  • Integrate in your PDF and/or data workflow: data is output in a structured, reusable format for further processing, with access to the page coordinates of the extracted content.

Visit our knowledge base to find code samples, manuals, documentation and more.

You can also find its API here.

Try our code in our developer sandbox or use our free apps, all in our iText 7 Demo Lab.

Release Notes

https://itextpdf.com/itext7release

NuGet packages

This package is not used by any NuGet packages.

GitHub repositories

This package is not used by any popular GitHub repositories.

Version History

Version Downloads Last updated
2.1.9 118 10/22/2020
2.1.8 388 7/23/2020
2.1.7 594 4/20/2020
2.1.6 341 2/13/2020
2.1.5 491 12/2/2019
2.1.4 403 9/19/2019
2.1.3 2,844 4/19/2019
2.1.2 1,962 11/20/2018
2.1.1 874 9/18/2018
2.1.0 803 8/29/2018
2.0.3 427 7/20/2018
2.0.2 921 4/30/2018
2.0.1 527 1/31/2018
2.0.0 474 12/19/2017
1.2.1 416 12/12/2017
1.2.0 377 11/15/2017