Batch scanning, zonal OCR indexing, and enterprise data capture for handprinted forms and complex documents.

Why are the prices of OCR applications so different?

OCR software ranges in price from freeware all the way up to tens of thousands of dollars. What explains the difference between these applications? Here’s the breakdown:

  • OCR Freeware uses the SimpleOCR or Tesseract engines and provide limited scanning and output format capabilities. Recognition quality is generally poor except for the highest quality document images.
  • PDF OCR Converters provide good quality OCR engines like ABBYY, IRIS and OmniPage, but limit the output to searchable PDF files. These cost less than $100.
  • Standard OCR applications range from $100-$200 and provide full OCR capabilities including converting scans to Word, Excel, HTML and other editable formats.
  • Corporate OCR applications add advanced features like automated hotfolder processing, concurrent licensing and other features useful for business applications. Pricing for these is $200-$500.
  • OCR Servers provide scalable, enterprise OCR services for processing very high volumes of documents or providing OCR capabilities to users throughout the organization. Prices start around $1,500 and go up based on processing volume.
  • Enterprise Data Capture and Forms Processing applications are used to capture structured data from complex documents like healthcare claim forms and invoices that include things like tables, handwriting, checkboxes, and movable zones. These solutions can cost anywhere from around $1,000 to hundreds of thousands of dollars depending on the document volume and complexity of the project.

Creating forms optimized for handprint recognition

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

What are the best scanner settings for OCR?

Most OCR applications are optimized for 300 dots per inch resolution images.

While color is supported and most often performs better than black & white images, OCR algorithms will generally convert the color to B&W automatically as part of the OCR process. With color input, the dynamic conversion usually produces the best result, but not always.

Especially when an image contains stray markings, stamps, notes, colored paper or other elements that can throw off the binarization process, OCR results can be improved by paying careful attention to image processing settings and using a pristine black & white image for OCR instead of a color scan.

In forms processing and handprint recognition applications, guide marks in the form can often be removed during the scanning process, improving the OCR results when the software doesn’t have to distinguish between the form background and the words being recognized.

Using drop-out forms, traditionally printed in red or green and then scanned with a corresponding red or green light, automatically removes the form background during scanning and leaves only the text to be recognized. This can dramatically improve recognition results, especially for handprinted data.

Older, black & white scanners would require you to change out the lamps in order to perform color drop-out. All but the least expensive modern color scanners have the ability to enable drop-out colors in the scanner driver.

Advanced forms processing applications can perform color drop-out on-the-fly with scanned color images. Though this is generally not quite as accurate as scanning with a drop-out lamp enabled, it has the advantage of retaining a full-color original copy of the image with the form element and labels visible.

Using OCR to capture data from tables and reports

Data that repeats over and over again in a document can be OCR’d to Microsoft Excel, Google Sheets and other spreadsheet formats, or a SQL Database like Access, SQL Server, MySQL and Oracle.

Inexpensive Desktop OCR products like FineReader, ReadIRIS and OmniPage can automatically convert data from tables to Excel and other spreadsheets, as long as the columns are standard and don’t “overlap” such that different field values appear in the same column area, like when one row of each record represents one set of columns and a second row has additional column data.

Converted data will require some clean-up before it is usable in any database or software application, and it is difficult to convert large numbers of documents in batches this way. But it’s a good way to produce structured data from large single reports or small batches of similar report data.

For more complex tables, tables with similar data but different formats on different documents (like Invoices), tables with nested structure like header and detail rows, Enterprise Forms Processing software is required to turn these documents into structured data like XML, JSON or SQL database tables.

Using OCR with Robotic Process Automation (RPA)

Robotic Process Automation of Data Entry
Robotic Process Automation can simulate human user interfaces to allow code-free application integration for data entry workflows

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 […]

Using Artificial Intelligence to train OCR templates

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.

How to use Zone OCR when the data can be in different locations?

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.

Does ReadIRIS, FineReader or OmniPage support Zone OCR?

The “Pro” versions of most Desktop OCR applications support the creation of zone templates that can be used to OCR specific regions on batches of documents.

Most OCR applications have “Lite” versions that don’t have the ability to manually create zones so it’s important to get the correct version.

With these applications it is often not possible to output this data as “fields” in a structured data file like CSV, Excel or XML. What you typically get a text file for each document with a line of text for each zone. The zones are designed more for excluding regions you don’t want or manually overriding the detection of text, tables and images in the document.

If you need to capture specific data in multiple documents and output them to structured data files or a SQL database, Batch OCR Applications are the best option for this.

If you need to capture data formatted in tables and output to CSV or Excel, desktop OCR applications do this quite well as long as the tables have a regular format with well-defined columns.

To capture handprint, irregular tables, large numbers of data points, or data that doesn’t always appear in the same place on every page, Forms Processing software is what you need.

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