Automate Data Entry

FlexiLayout: How to capture a table using Repeating Group if table header is on each page

In some cases, we might have a table that we are not able to capture correctly using a traditional method – Table element. In such cases, we usually use Repeating Group element.

But what if we come across a multi-page document that has a table header on each page?

We can use two following methods to capture such a table using the Repeating Groups.

Using Absolute search area constraints

To limit the search area to the table area so that it doesn’t capture unnecessary text outside of the table, we can use Absolute search area constraints in the Search Constraints tab.

You can measure the area with the Measure Rectangle tool.

Using nested Repeating groups

Sometimes it might be not suitable to use the Absolute search area constraints method because other tables using this layout might have different positions and lengths of elements, thus making it not convenient to use the method, because you will have to re-measure the area every single time.

In such a case, you can use the nested Repeating group method.

1. Create the first, “main” Repeating group that will include the Table header and footer. 
2. Next, create the nested RG in the first RG. The relations are as follows: 
3. These are the main steps, other elements in the RG don’t need any specific settings and should be designed according to the needed results.

Additional information

FlexiLayout: Capturing a table using Repeating Group

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To get a first access token perform the initial authentification using the default client, one does not need to enter any passwords or client ID. The initial authentication is preconfigured. Just open a Swagger page (EU link or US link), click Authorize:

Select all scopes, and click Authorize again:

The password should be specified only for a custom client. A custom client can be created after the initial initialization.

References

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Handprint recognition applications can provide dramatically different results in terms of accuracy depending on whether the form is designed with intelligent character recognition (ICR) in mind.

Forms Processing applications like ABBYY FlexiCapture have a built-in form design tool with ICR-optimized field layout elements and rules that validate whether your form uses best practices for recognition. These forms can be automatically converted to recognition templates for scanning for data capture. This saves you dozens of hours of trial and error during the design process and even more in data entry once the filled in forms are collected.

Best practice recommendations for ICR and OCR forms include:

• Plenty of space between form elements and labels, at least 0.5cm / 0.25in
• Use drop out colors for form backgrounds when possible
• Hand printed characters should be constrained with boxes or combs to force filler to write legible, separated, printed characters
• Use check boxes instead of handprint when possible since these are nearly 100% accurate
• Use numeric codes instead of alphanumeric text when possible to reduce the number of possible characters and increase accuracy
• Use validation rules to check against possible values and flag data with incorrect values
• Check box fields can be used to verify the presence of signatures

Robotic Process Automation applications like UiPath and Blue Prism have revolutionized the way that enterprises provide systems integrations and automate repetitive tasks. For any task that involves data that comes on a document, OCR is needed to fully automate it.

An example RPA OCR workflow using an Accounts Payable Invoice automation would be:

1. Bot signs on to Vendor website
2. Bot navigates to Invoice Download page and downloads invoice batch PDF
3. PDF is handed off to FlexiCapture for Invoices RPA interface for data extraction
4. XML is returned to Bot containing header (invoice number, date) and line item data (items, quantities, pricing) for each invoice
5. Bot opens accounts payable data entry screen in accounting software
6. Data from each invoice is entered and submitted by the Bot

Since RPA simulates the clicks and keystrokes that would normally be made by a human operator, bots are able to interface with any software, database or website regardless of whether an Application Programming Interface (API) has been made available. This gets around the hardest part of most data entry automation processes–the need to write code.

ABBYY FlexiCapture integrates with RPA applications like UiPath and Blue Prism to perform OCR data capture services that can be called directly from a bot’s workflow.

ABBYY FineReader Corporate and FineReader Server can be integrated for full-text OCR.

Our OCR experts are also UiPath certified and can deliver end-to-end RPA OCR solutions for your project. Please […]

Modern Forms Processing applications have AI-based training algorithms that let users point and click on the location of data in their documents and create OCR templates automatically.

This bypasses the technical requirements of creating complex OCR templates, especially for varied documents like Invoices where the data doesn’t always appear in the same place.

But how good are these AI-based training systems?

In our experience they work well when you have:

• Good quality scanned images
• Clearly labeled data
• Tables with regular columns

Point and click style training doesn’t work quite as well with:

• Poor quality images
• Data that appears within paragraphs
• Tables with overlapping columns, subtotal rows, etc.

These types of documents can still be captured with OCR but they will usually require an experienced technician to manually configure the template.

For natural language data like legal documents, a new artificial intelligence technology called NLP (Natural Language Processing) is available. These work by attempting to “understand” the language used in documents to interpret the location of data points based on meaning. ABBYY FlexiCapture also supports NLP-based training for these types of documents.

Modern Forms Processing software can use rules-based templates for locating data on documents based on label keywords, data types, regular expression pattern matching and other methods.

The most common example in business is an Invoice. Businesses receive invoices from 1000s of different vendors, each with important information like the Invoice Number, Due Date and Total needed to process the document, but each vendor invoice is formatted a little differently than the others.

Software like ABBYY FlexiCapture will look for keywords like “Invoice Number” or variations like “Inv #” and “Invoice No.” to locate the invoice number value on each invoice.

These applications are also able to capture complex table data and output to formats like Excel or a SQL Database, especially when it doesn’t line up into regular columns.

In recent years, artificial intelligence based training has made it possible to simply point and click on the location of data on documents as you process them and generate these templates automatically, dramatically reducing the need for ongoing expert help these systems require.