pdf2Data

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, addresses, purchase order numbers 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.

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.

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.