What Is OCR (Optical Character Recognition)?

Optical character recognition technology (OCR), or text recognition, converts text images into a machine-readable format. In an age of growing need for efficient data extraction and analysis processes, OCR has helped organizations revolutionize how they process and manage textual data from images. In addition to pictures taken by a camera, OCR is often used on scanned documents and image-only PDFs.  

The expanded use of artificial intelligence (AI) has led to more advanced implementations, such as intelligent character recognition. Let’s explore how the technology works, its applications in business, and what the future holds for OCR technology. 

How OCR Works

Even though most companies operate digitally, paper is still involved in many transactions. Many companies have documents like contracts, invoices, and other paper forms stored in physical files and folders. They’re seeing the benefits of having tools that allow them to quickly scan images to text data so other software platforms can read them. Let’s look at the stages involved in successfully extracting text from images via OCR. 

1. Image Acquisition

First, you need a way to obtain images for the OCR process. This often involves retrieving them from devices like cameras and smartphones. A high-quality image allows for a greater degree of OCR accuracy. Factors like resolution, lighting, and focus impact the overall quality of an image. 

Ideally, an image being put through OCR should have at least a 300 DPI (dots per inch) resolution for more accurate text recognition. DPI refers to the number of ink droplets a printer produces when generating an image. More dots allow for more detail to be seen on a printed image. 

2. Image Preprocessing

After acquiring an image, OCR software must clean and enhance it to make the text more recognizable. Below are some of the steps involved in that process. 

• Binarization: The image gets converted to a binary format, making every pixel black (for text) or white (for the background). Removing color and grayscale information removes complexity, making it easier for the OCR engine to read the image. 
• Noise removal: Anything unwanted, like smudges or random pixels, gets removed to keep them from distorting the text. 
• Skew correction: Any tilts or misalignments in a scanned image are corrected to avoid misinterpreting the characters.
• Deskewing and rotation: This process looks for places in the image that need to be aligned and rotates it clockwise or counterclockwise. This makes the image more uniform, improving OCR accuracy.
• Normalization: The brightness, scaling, and contrast of the image get adjusted to remove variations, which improves its quality. 
• Edge detection and smoothing: Any irrelevant details are removed, and the boundaries are enhanced. 
• Segmentation: The image gets divided into regions like text blocks, lines, and words. 
• Resizing: The image size gets adjusted to make it optimal for OCR engines. 

3. Text Recognition

After preprocessing, the image’s pixel patterns get converted to machine-readable text with recognition algorithms. All the patterns uncovered during the previous stage get interpreted as words, characters, and even sentences. Let’s look at some of the most common techniques involved. 

• Pattern matching: The OCR program compares characters in the image to a database of predefined character templates and looks for the closest match. 
• Feature extraction: Image characters get broken down into features like loops, curves, and lines to help the OCR engine recognize characters and symbols when matched in a database. 
• Machine learning (ML) and neural networks: More advanced OCR systems get trained on datasets using neural networks to analyze image patterns. Continuous learning makes the OCR engine more accurate after each cycle. 

4. Post-Processing

This stage involves applying techniques to correct errors in the raw OCR output, making the text more accurate and readable. 

• Spell checking/contextual analysis: The OCR system compares the output to a dictionary. Any words not found get flagged for correction. 
• Layout retention: Algorithms preserve a document’s original layout to ensure that text is positioned correctly and that all columns, images, and tables remain aligned. 

Types of OCR Technologies

Four basic types of OCR programs are used to convert images to text. 

• Simple OCR: Also called traditional pattern recognition, this process analyzes every scanned character and compares them to predefined templates kept in a database.
• Optical mark recognition (OMR): Helps OCR systems identify marks like checkboxes, bubbles, logos, and form signatures.  
• Intelligent character recognition (ICR): A more advanced form of OCR capable of handling more complex text like handwriting and variable fonts
• Barcode recognition: This technology interprets symbols like barcodes and QR codes to extract relevant information. 

Applications and Use Cases of OCR

Below are examples of how companies in various industries apply OCR in business. 

Software Testing

OCR helps UI testers automate test suites to validate software that displays information in a non-standard format. For example, the OCR tool can take screenshots, extract text, and compare it to expected outputs, mainly when using AI in continuing testing. 

Banking and Financial Services

Many banks use OCR to read information from checks, including the account number and payer details. It also reviews loan applications, processes invoices, and manages receipts.

Healthcare Data

Many healthcare providers use OCR to convert paper records to electronic health records (EHRs). This reduces manual errors in essential documentation like lab reports, prescriptions, and patient histories.  

Legal Documentation

Legal firms use OCR to convert physical case files, contracts, and legal briefs into digital formats to make them more searchable. This reduces the time spent searching for documents relevant to a case.

Assistance for the Visually Impaired

Text-to-speech (TTS) systems use the information converted from OCR software to read text aloud so users can access printed materials. Screen readers also use OCR output to help the visually impaired navigate digital content. 

Benefits of OCR

Digitizing paper documents makes it easier to control who gains access to them. Companies can place access controls around storage repositories. Files also remain accessible in a natural disaster that destroys paper documents. OCR also eliminates searching through large file cabinets for relevant records. 

Converting images to digital files means companies no longer need to store large quantities of paper documents. This reduces overhead costs and frees up financial resources for other business purposes. 

Challenges and Limitations of OCR

One of the biggest challenges with using OCR is managing more complex images containing handwriting. AI and machine learning advancements have helped OCR software obtain more accurate information from these formats. However, only so much OCR can do with the source is a poor-quality image. 

The variability of handwriting makes it hard for OCR systems to handle, especially when it doesn’t follow traditional patterns. That’s especially true when interpreting doctor notes in a patient file. Non-standard symbols and fonts, like those found in scientific papers, can be challenging to interpret. 

OCR systems with more advanced machine learning can use a lot of memory and processing power. That can slow down OCR applications, impacting work processes in high-volume environments. A company looking to convert years of documents must invest more server capacity to handle the load. 

OCR and Ranorex Studio

While Ranorex Studio does not natively support OCR, its object recognition and GDI capture tools allow for detailed GUI testing. For teams requiring OCR functionality, integrating third-party solutions can extend Ranorex’s capabilities, ensuring that your testing covers all aspects of application quality. Want to see it in action? Request a demo today..