Skip links

From Scans to Insights: The Magic of Optical Character Recognition (OCR)

How did we move from paper to digital solutions? What role does optical character recognition (OCR) play here, and how important is it? What does the future bring to us, and what should we focus on today?

When discussing OCR, a key question emerges, what kind of OCR do we need to stay competitive? Is the standard OCR sufficient, or do we need to step it up with AI?

In this blog, we will explore the significance of OCR software in document management systems (DMS) and compare standard OCR with OCR assisted by Artificial Intelligence (AI).

What is OCR software and how does it work?

OCR refers to technology that analyzes, reads, and extracts text from scanned documents or images, transforming it into machine-readable text. This conversion allows the automation of data entry tasks, reduction of manual errors, and enhancement of overall productivity.

It’s used for digitizing physical documents, such as invoices, loans, payment slips, forms, ID cards, passports, etc. The end goal is to store the document and its metadata in the DMS/ERP system, so it is available for everyone with the appropriate access.

But how exactly does OCR work?

The scanned image is analyzed for light and dark pixels. The dark ones create characters that need to be recognized, while the light ones are recognized as background. The OCR engine extrapolates the dark portions of the image, creating characters, and then tries to match them with its trained data.

Characters from the train data
Characters from the trained data

Characters gathered from the image
Characters gathered from the image

Some additional steps are needed to get the best possible results, including pre-processing and post-processing. 

Pre-processing creates the best possible image so that the OCR engine can better extract the characters. Image processing methods can include deskewing, removing lines, despeckle, and background removal.

Post-processing is used to further improve gathered data, e.g., data formatting, removing spaces, deleting characters, etc.

Limitations of OCR?

Standard OCR is good for basic forms, invoices, passports, and so on, but it has limitations when faced with different font styles, dense text, and handwriting, as it can wrongly detect a character. For example, standard culprits are 1, upper letter i, lower letter L, or 0, D, and O. The main issue in standard OCR is context, something that the human mind doesn’t have trouble with, but now, thanks to AI, it is possible to further enhance OCR capabilities.

AI OCR

For starters, AI evolves using computers to do things that usually require human intelligence. Humans can see with their eyes and process what they see.

AI is a computer science field that includes many different terms, such as NLP (Natural Language Processing), ML (Machine Learning), Computer Vision, Speech Recognition, etc. Computers require lots of data to achieve these capabilities. Large AI sets allow AI algorithms to identify patterns, make predictions, and recommend actions.

Potential benefits of AI OCR over standard OCR are:

✔️ Improved Accuracy: AI significantly boosts OCR’s text recognition accuracy, even in challenging conditions.

✔️ Improve Data Analysis: AI-OCR combinations extract text and analyze the data for deeper insights. They can identify patterns and trends that are invaluable for business intelligence.

✔️ Faster Processing: AI algorithms process data much faster than traditional OCR.

✔️ Reduced Manual Intervention: AI’s automation reduces the need for manual data entry and double-checking compared to standard OCR.

✔️ Plug-and-play processes: Creating traditional OCR processes for zone or text recognition can be time-consuming. AI allows models to be trained simply by clicking on the data that we want the system to extract, meaning less time is spent configuring the whole process.

The Key Components of OCR in Document Management Systems (DMS)

OCR’s functionality within a DMS can be broken down into four key steps:

👉 Image Preprocessing

Before the OCR process can begin, the software enhances the quality of the image to improve accuracy. This can include adjusting brightness and contrast, deskewing the image, and removing noise or distortions.

👉 Text Detection

The software identifies areas of the image that contain text. This involves distinguishing between text and non-text elements such as images or graphics.

👉 Character Recognition

The OCR software uses advanced algorithms to analyze the text areas and recognize individual characters. This step often involves pattern recognition and feature extraction techniques to accurately identify letters, numbers, and symbols.

👉 Post-Processing

After recognizing the characters, the software applies various techniques to improve accuracy and format the text correctly. This can include spell-checking, grammar correction, and layout analysis to ensure the output matches the original document as closely as possible.

What kind of OCR do we need in DMS?

The OCR technology needed for DMS must be both fast and highly accurate. For example, a document containing about 300-500 words takes approximately 10 minutes for a person to copy manually. On the other hand, OCR can complete the same task in 10 seconds.

Accuracy can be challenging for an OCR engine, but today’s OCR engines achieve high accuracy rates and can surpass the precision of manual input. It brings many other benefits, such as cost savings, increased document security, and improved compliance.

Who Stands to Gain the Most from Optical Character Recognition?

OCR technology is a game-changer for industries dealing with large volumes of paper-based or image-based documents. It helps automate document management, improve searchability, and streamline workflows. Here are some key sectors where OCR delivers the most impact:

1. Enterprises

OCR helps enterprises manage high volumes of contracts, purchase orders, financial reports, and employee records by automating data extraction, making document retrieval faster and more accurate.

2. Financial Institutions

Banks and financial institutions use OCR to process loan applications, tax forms, credit reports, and account statements, speeding up approvals while ensuring regulatory compliance.

3. Legal Industry

Law firms use OCR to convert contracts, court filings, legal briefs, and case documents into searchable digital files, streamlining case management and improving document organization.

4. Retail and E-Commerce

Retailers use OCR to process invoices, purchase orders, product catalogs, and receipts, enhancing the efficiency of order processing, invoicing, and inventory tracking.

5. Healthcare

Hospitals and clinics digitize patient records, prescriptions, lab reports, and insurance forms with OCR, improving data access and accuracy for faster and more reliable patient care.

6. Human Resources (HR)

HR departments utilize OCR to manage employee records, resumes, and onboarding documents, improving data accuracy and speeding up recruitment and employee management processes.

Let’s Conclude

We live in an era of rapid technological progress, where convenience and efficiency are more critical than ever. Time remains the biggest challenge we face, and the phrase “time is money” has never been more relevant.

By integrating OCR into DMS, organizations can significantly reduce the time spent searching for information, reduce storage costs, and provide simultaneous access to documents for multiple users across different locations.

Leverage the expertise of over 200 professionals to optimize your business

Start now
Home
Account
Cart
Search