OCR training algorithms use artificial intelligence to improve recognition accuracy and automatically identify common data elements based on learned context.

OCR training refers to one of two things:

  1. Tuning the OCR Engine to improve recognition of new fonts, languages, or handwritten text.
  2. Training data capture software to identify the correct location of fields on various related documents.

OCR training machine learning algorithmsThe first type of OCR training was a commonly used feature in early desktop OCR applications. However, modern OCR engines are trained on huge sample sets during development and manual training is more likely to decrease accuracy than anything. It is rarely used outside of the development environment.

The second type of OCR training is used by enterprise data capture applications to automate the creation of recognition templates. The most common application is accounts payable invoices, where every vendor has their own layout and formatting but share the same data fields. These systems can “learn” from user feedback, improving the recognition accuracy until all fields are consistently read.

Data capture analyzes a document and grabs only the data you need for your business purposesField position training usually starts with a generic template that can identify the fields using the most common labels. Whenever a field is missed or read in the wrong position, the user highlights the correct field position on that document during a manual review. The new position is used by the machine learning algorithm to generate an updated template that correctly identifies the fields on that sample. If the document has consistent layout and decent image quality the template can be trained after just 2-3 samples.

More complex documents and documents that have a lot of layout variation can take many samples to train, and sometimes they can fail to train altogether. AI OCR training is not magic, and there will always be some cases where it is unable to consistently read a document correctly. If 100% accuracy is needed for these documents then it is important to choose a data capture platform that offers the ability to manually override the OCR training.

You don't need AI OCR Training and Machine Learning for thatMany newer OCR systems no longer offer the ability to manually create templates and rely fully on the machine learning function. While these systems can be easier to configure, they will never reach the level of accuracy that can be achieved by one that offers a manual override.

There are also many kinds of documents that can be easily parsed with simple pattern matching, or where an experienced user can create a template that works perfectly in just a few hours. This can save a lot of time, user frustration, and licensing costs compared to machine learning. It is important to know when OCR training is really needed, and the experts at Simple Software can help.

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.

Can OCR be trained for specific fonts?

OCR training was once a critical part of the conversion process. After a document was read, the operator would review the results to correct mistaken characters and these corrections would be used to train the engine so the next time you read a similar document the results are improved.

Modern OCR applications no longer rely on user training for accuracy unless you have very non-standard fonts. These engines have had decades of development and billions of samples used to train their algorithms. In most cases, the introduction of user training will only diminish the results for any documents that are different than the ones being trained.

The training functions still exist for these edge cases, but they are no longer an integral part of the OCR process.

Training in modern OCR is more likely to refer to enterprise data capture applications that use AI-based learning algorithms to find the locations of data points on documents with various different formats, such as invoices.

AI and Machine Learning in ABBYY FlexiCapture and Vantage

How to train NLP machine learning model

Today different industries face similar challenges as they seek to extract information from business documents, such as policies, e-mails and legal agreements – and most agree that is costly, time consuming and prone to errors with manual data entry.

In this video you will learn how to train NLP machine learning model in FlexiCapture to extract entities and text passages from Lease agreements.

Converting unstructured documents into structured data automatically makes this information available to your business applications while saving you time, money, and labor in the process.

FlexiCapture and Vantage Natural Language Processing (NLP)

How to train NLP machine learning model

Today different industries face similar challenges as they seek to extract information from business documents, such as policies, e-mails and legal agreements – and most agree that is costly, time consuming and prone to errors with manual data entry.

In this video you will learn how to train NLP machine learning model in FlexiCapture to extract entities and text passages from Lease agreements.

Converting unstructured documents into structured data automatically makes this information available to your business applications while saving you time, money, and labor in the process.

 

Adding a field which is captured by flexilayout to a NLP-trained Document Definition

You can add the new flexible layout as additional layout to the existing one.
To do that, please open the Document Definition Editor, go to the Section’s properties and load the new layout as additional FlexiLayout.

OCR Guide

Optical Character Recognition

During your foray into the world of document scanning, you’ve likely encountered the term “OCR” and may even know that it stands for “Optical Character Recognition“. But what exactly is OCR and how can you make the best use of this sophisticated and valuable tool?

We’re here to give you a run-down of what you need to know about Optical Character Recognition, answer any questions you might have, and recommend the best OCR software solution for your scanning project.

Table of Contents:

What is OCR?

What Is OCR Barcode Scanning Recognition SoftwareThe primary purpose of Optical Character Recognition  is to quickly and automatically scanned or photographed document images into machine readable text that can be searched for keywords or edited in a word processor.

In general, an OCR engine analyzes the pixel data of scanned images and searches for patterns resembling letters, numbers, and other symbols to create a digitized record of characters.

The biggest OCR engines employ huge Artificial Intelligence (AI) and Machine Learning (ML) models that have been trained on billions of documents collected over decades of development.

While the exact mechanics of this process can be complicated, OCR engines are a key automation tool for the digital age. It bridges the gap between knowledge stored on physical documents and digital data that can be edited, searched or parsed into structured data to automate data entry tasks.

OCR Output Types

Search Document OCR Recognized TextFull Page OCR converts the entire document into one of the following formats:

    […]

Robotic Process Automation

Introducing Robotic Process Automation

RPA stands for Robotic Process Automation and it represents a new approach to business automation that helps minimize the technical hurdles required for implementing new workflows.

Robotic Process Automation of Data Entry

Traditional business process automations rely on application programming interfaces (APIs) to allow systems to exchange data. This approach has two main drawbacks:

  1. The application vendor must make those APIs available
  2. A programmer needs to write custom code to interface with them

If your software vendor does not provide an interface for consuming the data you need to automate, then you’re out of luck. And even if they do, the development costs can eliminate the ROI if the transaction volume isn’t large enough.

RPA tools avoid the API problem by interfacing directly with the application user interface just like a human would do. They use artificial intelligence and machine learning to “watch” the operator perform a task within the application then creates its own program (called a “bot”) to mimic it. This means that:

  1. Bots can do anything a human can do within the application
  2. Users can create a bot without writing code

Practically speaking, an experienced robotic process automation consultant with programming experience is required to roll out an RPA solution enterprise-wide, and most users will only be able to automate small, routine tasks without assistance. Business-critical, high-volume automations will still involve coding. But RPA dramatically reduces the implementation time and avoids the need to retrofit APIs for software applications that were not designed to support them.

Using RPA with OCR Data Capture

UiPath Robotic Process Automation RPA OCROCR Data Capture is one of the most common business processes to automate with RPA. Taking data stored in paper or electronic documents and […]

Title

Go to Top