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:

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

The 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.

Field 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.

Many 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.

Total Cost of Ownership of an OCR Software

What is TCO (total cost of ownership)?

Total Cost of Ownership

The Total Cost of Ownership (TCO) is a financial estimate intended to help buyers and owners determine the direct and indirect costs of a product or service. It is a management accounting concept that can be used in full cost accounting. (Wikipedia).

TCO is a popular concept when it comes to comparing Software. It allows you to estimate the cost of using the OCR software and plan your automation strategy according to your possible workload and budget over time.

There are several aspects that are too circumstantial to be able to include in general cost estimates, but there are some that we know for a fact, and we can combine them in a total cost of ownership for each OCR scenario and compare them to the possible solutions on the market.

Front-End Cost

The first and most obvious element is front-end cost. This is the price that will be presented with the product, and the marketing team will be pointing towards special offers and discounts to cut it down. It is an important factor, but it is far from everything you would need to take into account. And yet, even here, there are different options. Some of the OCR solutions will offer you the “pay and forget” option of making one payment right here, right now. However, lately, the annual cost or subscription model has become more and more popular, allowing customers to spread costs over time and OCR creators to organize a steady flow of income.

Cost of Support and Maintenance

The second element of the total cost of ownership would be the cost of support and maintenance. Theoretically, it is possible to avoid […]

What is OCR?

OCR stands for Optical Character Recognition and is the technology that allows software to interpret text on scanned images. When this technology is applied to automating business data entry processes it’s referred to as OCR Data Capture.

Many are familiar with popular desktop OCR applications designed to convert scanned images to editable documents. When this process is applied to specific areas of the document containing data fields it’s called zone OCR. But OCR data capture software is more than just simple zone OCR. Modern applications use some or all of these technologies:

Handprint recognition (ICR or Intelligent Character Recognition) for forms processing.
Advanced rules-based templates for locating common data elements on pages with different layouts and formatting.
Artificial intelligence that is able to use point and click user feedback to train recognition templates automatically.
Natural language processing is able to interpret paragraphs of text and extract meaningful data from them.
Robotic process automation puts back office integration into the hands of power users instead of programmers.
Preconfigured form templates and business rules for common applications like invoice processing and healthcare claim forms.

80%

Using the OCR software enables enterprises to reduce the document processing time by as much as 80%

Benefits of using OCR

If not for the trees then do it for the savings on paper, toner, copiers and their services contracts, etc.

How much time is wasted searching for paper files? Digital documents can searched and viewed instantly from anywhere.

Paper is much harder to backup and restore than digital data.

Office square footage and off-site records storage adds to the cost of keeping paper documents.

Government mandates for records retention […]

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.

Aaron Tyrrell2022-06-21T14:38:46-04:00Tags: AI Classification, AI OCR Training, Artificial Intelligence, Automate Data Entry, Data Capture, Data Extraction, Electronic Data Capture, Extract Text from Image, Machine Learning, NLP, OCR Solution, OCR Text, OCR Training, PDF Text Extraction, Robotic Process Automation, RPA, Text Recognition|

AI and Machine Learning in ABBYY FlexiCapture and Vantage

How to train NLP machine learning model

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.

Aaron Tyrrell2022-06-21T14:35:53-04:00Tags: AI Classification, AI OCR Training, Artificial Intelligence, Automate Data Entry, Automated Invoice Processing Software, Automatic Classification, Document Classification, Invoice Automation Software, Knoxville Robotic Process Automation, Knoxville RPA Consultants, Machine Learning, NLP, OCR Training, Robotic Process Automation, RPA|

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.

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

The application vendor must make those APIs available
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:

Bots can do anything a human can do within the application
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.

How much do Robotic Process Automation systems cost?

RPA tools like UiPath have community editions that allow anyone to start learning and developing bots for free.

In production, the license is typically priced […]

aaron2025-04-22T12:16:26-04:00Tags: Accounts Payable Automation, Accounts Payable OCR, AI Classification, AI OCR Training, AP Invoice Scanning, Artificial Intelligence, Automate Data Entry, Automated Invoice Processing Software, Batch OCR, Batch OCR Software, Cloud OCR, Data Capture, Data Extraction, Document Management, Document Processing Software, Electronic Data Capture, Human-in-the-loop verification, Invoice Automation Software, Invoice Workflow Software, Knoxville Robotic Process Automation, Knoxville RPA Consultants, Knoxville TN OCR Solutions, Knoxville UiPath Consultants, Machine Learning, NLP, OCR Engine, OCR Library, OCR SDK, OCR Solution, OCR Training, PDF Data Extraction, Procure to Pay Solutions for Labs, Receipt Scanning, Robotic Process Automation, RPA, Scan Handwriting to Text, Scan Invoice, Scan to Excel, Server OCR|

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.

aaron2021-01-24T18:39:27-05:00Tags: Artificial Intelligence, OCR Accuracy, OCR Character Recognition, OCR Text, OCR Training, Optical Character Recognition, Text Recognition|

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.

aaron2021-01-25T09:14:20-05:00Tags: Artificial Intelligence, Automate Data Entry, Batch OCR, Batch OCR PDF, Data Capture, Data Extraction, Extract Text from Image, Forms Processing, OCR Training, PDF Data Extraction, Server OCR, Zonal OCR, Zone OCR|