iText pdf2Data for PDF processing
iText pdf2Data is a solution to easily recognize and extract data from documents in a structured, reusable format. It is available for Java and C# (.NET), and as a CLI version.
It offers a framework to intelligently recognize data inside PDF documents, based on selection rules that you define in a template. This offers significant advantages over AI-based alternatives which need extensive training to recognize documents.
And thanks to the intuitive browser-based pdf2Data Editor (also available as a Docker image), anyone, from marketers to information managers to HR staff, can create and update templates. You don't need to be a developer to benefit from using iText pdf2Data.
Why use iText 7 pdf2Data?
Data is an important commodity, and you may have more than you realize locked inside your PDF documents. Collecting this data manually could take a lot of time and resources, with the risk of input errors or security issues to consider.
With iText pdf2Data you can automate the data capture process and extract data in a secure way. By reviewing documents against your template to validate the recognition process is correct, you can also ensure consistent results.
If your documents are not PDF, then iText has you covered. The iText 7 add-on pdfOCR turns scanned documents and images into PDF (or PDF/A-3u if you need long-term archiving compliance) ready to be processed by iText pdf2Data.
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 in Java and .NET (C#) or simply using the CLI.
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 Editor. 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
iText pdf2Data uses open standards to facilitate integration, which makes integrating it into existing workflows easy and fast. In addition to the easy to use pdf2Data Editor, it also includes developer-focused SDKs for Java and .NET (C#) as well as a command line interface. PDF data processing for the 21st century.
Better than AI-based alternatives?
Since the content recognition is based on selectors you define in the template, iText pdf2Data requires no prior training to recognize and extract data. The data recognition uses on a number of rules, which need to be defined in advance per each data field. Typical rules use all details from the PDF document, and can be combined to help ensure accurate data extraction.
Core capabilities of iText 7 pdf2Data
iText 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 data field selectors. Each selector uses a different way of identifying the information that is important. Selectors can also be combined to fine-tune the data identification and capture depending on your requirements.
The data is output in a structured, reusable format for further processing, with access to the page coordinates of the extracted content.
Extract data from PDF documents
Leverage iText's high-fidelity content extraction for recognition of text, images, and other content.
Intuitive extraction configuration
iText 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.
What iText pdf2Data does
Many PDF documents businesses need to process, such as registration forms, invoices etc. follow a common structure. If we take the example of an invoice document, the invoice number, supplier address, purchase order number and similar document elements tend to be located in one place, and only the content such as item descriptions, quantities and cost of items change from invoice to invoice. By using an example invoice as a template, it is possible to define areas of the document where the data you want to capture is located and categorize it.
iText pdf2Data offers an easy way to extract data from such PDF documents by defining areas and rules in a template which correspond to the content you want to extract. The template can then be visually validated with other documents to confirm data is recognized correctly, before being parsed by the pdf2Data SDK to process all subsequent documents matching that template.
Unlike AI-based alternatives, you don’t need hundreds of samples and intensive supervision to train the recognition process. The content recognition is controlled by the template you configure, meaning no training is required before you can begin extracting data. You only need one example document to enable data extraction from all subsequent documents.
AI recognition has other disadvantages too. Any changes to the required output (such as adding a new field) will require models to be retrained, and multiple language support is minimal at best. Documents using the same layout but containing content in different languages can give wildly inconsistent results.
iText pdf2Data on the other hand suffers from none of these drawbacks. Making modifications to templates is quick and easy, and it offers excellent language support. It also provides powerful table recognition functionality, which is one of the primary shortcomings of other data extraction solutions.
How iText pdf2Data works
By using the intuitive browser-based pdf2Data Editor, it’s easy to create a template for data extraction. Simply create a template PDF based on a sample document, by defining data field selectors for areas of interest. Selectors are configurable rules to detect different types of content for extraction.
There are approximately two dozen selectors to choose from which enable iText pdf2Data to intelligently recognize and extract text, and other content such as images or barcodes. The selectors can be configured to detect:
- page range and the position on the page
- specific font styles, font color, and text patterns
- fixed keywords next to the data
- automatic recognition of table structures
In addition, many selectors can be combined to fine-tune the detection parameters.
Your extraction template will then be used to parse all future PDFs matching the template. Using the pdf2Data Editor, you can upload a document to test your extraction template and make sure the data field selectors are configured correctly to recognize the data you require.
Similar to our document generation solution iText DITO, iText pdf2Data allows anyone to leverage iText's powerful PDF capabilities, not just developers. It's simple to create or refine document templates to recognize and automatically extract data, which can then be easily reused by whoever needs it. By intelligently extracting data from documents in a smart and structured way, the data can easily be repurposed for analysis, reports, or whatever you want.
Developers are only needed to deploy the pdf2Data Editor and integrate the pdf2Data SDK into your document workflow. From then on, you can configure a template, verify the data, and set iText pdf2Data to work.
You can find installation instructions, tutorials, and detailed documentation for all data field selectors in our Knowledge Base.
Here you will find the needed resources to install, configure and use the iText pdf2Data components. If you’re looking for a demonstration of how iText pdf2Data works, make sure to request a demo where you can get a guided presentation of its functionality.
Now you have the data extracted, insert it in a template-based solution
That's template-based data extraction done and dusted. Are you interested in a template-based, collaborative solution to create PDFs from data?