From OCR to AI: The Evolution of OCR Technology

For some, optical character recognition (OCR) was the way of the future, but how has OCR changed throughout history, and what does the future hold? Let us take you through the evolution of OCR technology and hear from one of our AI experts, Andrew Bird.

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For some, optical character recognition (OCR) was the way of the future, being introduced back in the late 1920s by physicist Emanuel Goldberg through his statistical machine. Being able to extra words and form sentences from images, OCR has been in use by a wide variety of companies and industries around the world to eliminate manual processing of documents.

More than 90 years later, OCR is now seen as legacy technology with the introduction of Artificial Intelligence (AI). So, is OCR still relevant in the 21st century? What challenges did it face and why was it heavily impacted by AI? Let us take you on a journey through the evolution of OCR technology and hear from one of our AI experts, Andrew Bird, about how AI has impacted OCR technology.

What is OCR?

Optical character recognition, also known as text recognition, as defined by IBM, is a technology that extracts and reuses information from scanned documents, images captured by a camera, and PDFs that contain only images. By identifying letters in the image and converting them into words and sentences, OCR software allows users to access and edit the original content of a scanned document or image without the need for manual data entry.

OCR systems combine hardware like optical scanners or specialised circuit boards with software to convert printed documents into machine-readable text. Artificial Intelligence (AI) can enhance OCR software by enabling more advanced intelligent character recognition (ICR) capabilities, such as recognising different languages or handwriting styles.  

This technology is commonly used to convert physical documents, such as resumes, legal contracts, historical documents, invoices and more, into editable PDF files. This allows users to easily search, edit and format documents as required.

What Types of OCR Systems Exist?

There are many different types of OCR services that are used for many different reasons. As defined by AWS, some common types of OCR include simple optical character recognition software, intelligent character recognition software, intelligent word recognition and optical mark recognition.  

Simple Optical Character Recognition Software

Simple OCR software functions by saving templates with various font, text and image designs. It utilises pattern-matching algorithms to compare text images to its internal database, character by character. When the text matches word for word, it is known as optical word recognition. However, due to the magnitude of font and handwriting variations, basic OCR technology has constraints.  

Intelligent Character Recognition Software

Intelligent Character Recognition (ICR), considered a more advanced form of OCR, enhances the capabilities of OCR technologies by allowing accurate reading of handwritten text and characters. Unlike simple OCR, ICR processes images character by character, resulting in quick and efficient output. Users can not only search and classify handwritten documents with ICR, but also convert them into machine-readable data.

Intelligent Word Recognition

Intelligent word recognition systems operate similarly to ICR, but instead of breaking down images into characters, they process full word images.

Optical Mark Recognition

Optical mark recognition software can detect logos, watermarks and other symbols within a document.

OCR and AI Data Extraction: How it Works

Optical Character Recognition is the first step used to scan an original document and/or image. A 2016 report, A Survey on Optical Character Recognition System, states that the process of OCR is comprised of six different phases. After a document and/or image has been through an OCR system, other AI models then read the OCR results to extract the necessary data.

How Does OCR Work?

  1. Image Acquisition – The first step is to capture the image of the document that you want processed from an external source, such as through a camera, scanner or other piece of hardware.  
  1. Pre-Processing – Once the image of the document has been captured, the second step is pre-processing. This is performed to improve the quality of the image, such as removing noise, thresholding and extracting the image base line. Pre-processing is completed by the OCR software.
  1. Character Segmentation – The third step involves taking the characters from the captured image of the document and separating them so they can be passed to the recognition engine.
  1. Feature Extraction – The segmented characters are then processed to extract different features. Based on these features, the characters are recognised. For example, a character (the letter a) will be recognised as the letter a as the OCR system utilises pattern-matching algorithms to compare the characters to its internal database.
  1. Character Classification – The fifth step is when the OCR service maps the features of the segmented characters to different categories and classes, ensuring that the characters are then transformed into sentences.
  1. Post-Processing – The final step after character classification is to improve the accuracy of the OCR results. The extracted data will never be 100% accurate, so post-processing techniques may include deploying a spell checker and dictionary to improve accuracy. After post-processing, you should successfully have a digitised version of the captured document.
The six phases of optical character recognition (OCR), including acquisition, pre-processing, segmentation, feature extraction, classification and post-processing.
The six major phases of optical character recognition (OCR).

How Does AI Data Extraction Work?

In order for AI technology to extract data from a file, a text layer is required to categorise and structure the data. Therefore, files uploaded to the system without a text layer will first need to be processed through an OCR system.  

Once the file is uploaded, the deep learning-based OCR AI technology can read and capture data from various accepted formats, such as PDF, JPG or DOCX. This form of data extraction is generally called intelligent document processing. The system will then automatically extract structured data from the file, which can be exported into a CSV file or integrated into software like an ERP or CRM system. This automated data extraction process is faster, more accurate, and allows for easier utilisation of the extracted data.

OCR vs AI Data Extraction: Advantages and Disadvantages

Since its introduction in the 1920s, OCR technology has transformed the way organisations and individuals digitise and process text, offering numerous advantages in terms of efficiency, accessibility and cost savings. However, like any technology, OCR is not without its drawbacks.  

OCR’s integration with AI technology has substantially improved turn-around time, accuracy and data interpretation, however there is still a long way to go. Here is a comparison of the advantages and disadvantages of manual document processing, optical character recognition and intelligent document processing/AI data extraction.

A comparison chart between manual document processing, optical character recognition (OCR) and AI data extraction/intelligent document processing.
Comparison chart: manual document processing vs optical character recognition (OCR) vs AI data extraction/intelligent document processing.

How OCR Has Transformed Throughout History

OCR technology has changed and adapted throughout history, from the early concepts in the 1920s to the present, with OCR services now integrated with Artificial Intellegence (AI) technology.

Andrew Bird, Head of AI at Affinda, says that AI has significantly revolutionised Optical Character Recognition technology.  

“Prior to the integration of AI, OCR systems were limited in accuracy, struggling with different fonts, handwriting, or any text presented in a less-than-ideal condition. This lack of precision made OCR impractical for many use cases, especially where accuracy was non-negotiable.  

However, with the advent of AI, OCR technologies have witnessed immense improvement in recognising complex patterns and deciphering texts from a myriad of backgrounds and formats. AI algorithms, through machine learning and deep learning, continuously learn and adapt, significantly enhancing OCR's accuracy and making it a viable option across various applications.”

Here’s how OCR has transformed throughout history:  

A timeline of the history of optical character recognition (OCR), from early concepts in the 1920s through to present-day AI-powered document processing.
A timeline of the history of optical character recognition (OCR), from early concepts through to present day.

The Future of OCR

So, what does the future of optical character recognition look like? Through advancements in speed, accuracy and the integration of AI, OCR continues to enhance various organisation’s document processes.

“Looking ahead, the role of OCR technology will undoubtedly evolve over the next 20 years. As business processes become more digital and paper documents less common, the direct need for traditional OCR might lessen. However, OCR will not be phased out completely. It is more likely to integrate more deeply with AI to process information in new and innovative ways.” says Mr. Bird.

A 2019 Forbes article, The Future of OCR is Deep Learning, states that “OCR is finally moving away from just seeing and matching. Driven by deep learning, it’s entering a new phase where it first recognizes scanned text, then makes meaning of it. The competitive edge will be given to the software that provides the most powerful information extraction and highest-quality insights.”

It’s difficult to predict with certainty what the future of OCR holds, but with advancements in AI-powered OCR technology, we could see improved accuracy, more seamless integration with everyday devices and applications and real-time recognition of text in videos, live streaming, or augmented reality applications.

“The future points towards multimodal models, which process documents as images rather than text. These models, by understanding the context and nuances of visual data, are expected to become the preferred technology, offering a more holistic approach to document analysis and interpretation.” says Mr. Bird.

Why Your Business Needs OCR and AI Data Extraction Technology

In today's rapidly evolving digital landscape, the need for efficient and accurate document processing capabilities has never been more critical for organisations across varying industries. Traditional OCR technology, while useful in the past, is quickly becoming outdated and unable to keep up with the growing demands and complexities of modern document management.  

As a result, many organisations are turning to intelligent document processing and AI data extraction solutions to address these challenges. If you’re yet to experience the benefits of integrating AI into your document processes, either through an off-the-shelf solution or an open-source OCR tool, you’re missing out. Here are the top four reasons why you need to make the switch.

1. Accuracy

Compared to manual data extraction and OCR, AI data extraction tools are close to 100% accurate, so you can always trust that automatically extracted data from any document you upload to the system is going to be correct.

“The accuracy of OCR has improved to the point where it is now comparable to human performance. This advancement means businesses can rely on OCR for critical tasks, from automating data entry to extracting information from documents with confidence. Looking ahead, AI-powered OCR is expected to surpass human accuracy, offering unparalleled efficiency and reliability.” says Mr. Bird.

2. Less Manual Data Handling

Manual data handling increases labour costs due to inefficiency and the requirement of more resources, is time consuming leading to slower workflows and potentially delaying decision-making and is more susceptible to errors caused by human oversight or inaccuracies in data entry.

“AI-powered document processing solutions significantly reduce the need for manual data handling, minimising errors, saving time, and allowing employees to focus on more strategic tasks. By embracing AI-powered OCR, businesses can enhance their operational efficiency, improve data accuracy, and pave the way for innovative document processing solutions.” says Mr. Bird.  

3. Scalability and Flexibility

Intelligent document processing solutions are highly scalable and can be easily integrated with existing systems and business workflows. This makes it easier for organisations to adapt to changing business needs and scale their AI technology as needed. An AI document processing solution can be the perfect way to automate tedious tasks and reduce costs for organisations that process a large volume of forms or documents every day, such as those in the HR, finance and insurance industries.

4. Increased Compliance and Security  

AI-powered OCR solutions often come with advanced security and compliance features, such as encryption, access controls, and auditing capabilities, to help organisations protect sensitive information and ensure regulatory compliance.  

Ready to Embrace Advanced AI Document Processing Technology?

Making the switch from outdated OCR technology to intelligent document processing and AI data extraction solutions can help your organisation improve accuracy, efficiency, capabilities, scalability, and security in your document processing operations. This can ultimately lead to cost savings, improved data quality, and better overall business outcomes.

At Affinda, we provide world-leading AI document processing solutions to help streamline your business workflows, no matter your organisation’s industry, size or needs.

Start a free trial today or book a demo to discuss your unique business requirements.

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