What is Optical Character Recognition?

What is OCR

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Optical Character Recognition (commonly known as “OCR”) has come a long way in terms of both speed and accuracy in a short time.

This technology is used to automate complex document processing workflows. This is a huge benefit in certain industries and jobs, especially those that handle large volumes of documents like order forms, invoices, receipts, and contracts. Businesses are saving tons of time and money by leveraging OCR in their routine tasks.

In this article, we’ll discuss what exactly OCR is and some common use cases.

What is Optical Character Recognition?

OCR is a technology that extracts information from a document and turns it into searchable and editable data. OCR can be used on physical paper documents that need to be scanned or on digital documents like pdfs or photos. OCR allows us to digitize the information that is contained within the document. OCR extracts the text and turns it into readable data that can then be leveraged by other systems, like an ERP or a content management system for further processing or analysis.

OCR is commonly used in invoice processing, legal document searchability and transcription, and sales order processing. OCR leverages machine learning and artificial intelligence to further minimize the amount of human intervention that is needed. AI and ML are put to use so that OCR can recognize more document types and languages and even mimic the way that the human brain recognizes patterns and context.

Let’s dive into how OCR makes this happen.

How does OCR Work?

OCR first analyzes the structure of the document image by identifying several elements in the document, including:

  • The text area,
  • The lines of structured data,
  • The edge and border spacing.

Optical Character Recognition scans the image for all possible document elements. Once it has scanned the image and loaded all the characters, they are then rendered into high contrast maps known as ‘bitmaps’.

The next step is ‘bitmap processing’ by many algorithms, the most common being Pattern Recognition.  Like any Machine Learning algorithm, Pattern Recognition is trained on millions of letters, digits, and characters in all their possible representations. These pattern recognition algorithms compare the identified character and then find the closest matching one.

Another common algorithm is the Feature Analysis Algorithm which seeks to identify specific strokes associated with each character’s formation.

From the moment you scan the text-laden image or document, these algorithms begin to run, until the document is transformed into a searchable and editable copy on your desktop.

How AI-driven OCR continues to improve

OCR continues to be enhanced year after year. In the early days of Optical Character Recognition technology, there were major limitations:

  • OCRs needed to be manually guided,
  • Outputs had to be thoroughly reviewed and often heavily edited,
  • OCRs only worked slightly faster than a human.

Today, Optical Character Recognition can find and read a license plate of a car driving by at high speeds. It offers the speed, automation, efficiency, and sophistication that businesses need to automate the tons of document-handling work they do.

OCR combined with AI has proven to be the winning combination: it is what differentiates the letter ‘O’ from the digit ‘0’. By analyzing broader contextual and linguistic patterns, AI corrects the mistakes that may slip through the ‘eyes’ of an OCR.

The original use cases for OCR focused heavily on written texts and printed characters, often on paper, but this technology promises to go far beyond that in the future.

Think about the possibility of an overseas traveler using an Augmented Reality (AR) app to understand street names, store signs, and billboards, all in a different language they’ve never spoken. Think about the possibility of self-driving cars reading street signs at night, in cities the passenger has never visited before. The sky is the limit for this incredible technology when it is paired with other artificial intelligence tools and machine learning.

Example Use Case: Invoice Digitization via OCR 

Most businesses receive multiple bills and invoices each day, which need to be reviewed, recorded, and eventually paid on time. In today’s highly digital world, many of these documents are emailed but many are still also physically mailed.

With stacks of paper documents and hundreds of digital documents received, how does the accounts payable department manage them efficiently?

The answer lies in the use of Optical Character Recognition which digitizes bills and invoices instantly. It helps to transfer them directly to the accounting database where it allows the AP clerk to manage them all in one location and automates data entry.

From scanning individual invoices or bills, to automatically posting the details to the accounting system, large parts of the accounts payable process are now automated thanks to OCR. This means your accounting team does not have to review each bill and manually type the data into a system one by one.

OCR also allows for easy searching – since OCR technology can read and store text from images, you no longer need to remember the name or location of the file to access it. You can now find it by searching for relevant embedded text within the file.

Optical Character Recognition OCR to record receipts

Example Use Case: Legal Document Processing via OCR 

The legal field is another industry that is becoming highly reliant on OCR to improve its daily processes.

Legal assistants, stenographers, and secretaries can attest to how tedious and time-consuming it is to transcribe physical documents (old contracts, images of documents, even hand-written information) to digital text.

With OCR in place, the process of converting legal paperwork into a digital, machine-readable format has become easy and efficient.

While case laws and judgments are typically available in soft copy, it is the multitude of other documentation where this becomes extremely useful, including order sheets, evidence, day-to-day proceedings, and written and miscellaneous statements that may be available in paper copy or pdf only.

With OCR tools becoming readily available as Android and iOS apps, converting all these documents into a soft copy is as easy as taking a picture with your cell phone’s camera.

All you need is a picture of the document, the OCR app, and (typically) an internet connection to transform this image into a searchable, editable, machine-readable soft copy.

OCR to convert legal documents

As you can see, industries and professions that rely heavily on paper documents have found great ways to leverage OCR to make their processes more efficient. Using Optical Character Recognition allows accounts and legal professionals to spend less time on data entry and data accuracy and more time on high-value work, like analyzing this data.

These are just two common use cases – most businesses can find ways to use this technology to make their day-to-day more efficient and their employees’ lives easier!

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