Optical Character Recognition (OCR): Meaning, How It Works, Benefits, Applications, & More!

In the context of data entry and data extraction, you must have heard of the term Optical Character Recognition. Well, it might sound fancy, but it is a simple concept. When you have images bearing key data, they lack key features. For example, you cannot edit the data, search for specific bits of information, or perform analytics on it. The simple solution here is OCR software. To learn more, refer to the article below.  

What is Optical Character Recognition?

What does OCR stand for? If that is your question, OCR is Optical Character Recognition. This is a technical term and simply refers to an image-to-text conversion! In this conversion, images of text are turned into machine-readable text formats. 

For example, suppose you have a hard copy of some receipt, and you take a picture of it. Optical character recognition software converts this image into a completely text-based format for faster data processing. 

Do you know? All the top data entry outsourcing companies use OCR software. They often have to deal with images of text!

How Does Optical Character Recognition Work?

The optical character recognition software works in 5 main stages. These are briefly mentioned below in simple language. In short, it involves image acquisition, preprocessing, text recognition, layout recognition, and postprocessing. 

1. Image acquisition: OCR software is provided with all the images of text. These images are scanned through, and then the black areas (words or data) are recognized against the white background. 
2. Preprocessing: Here, the image is cleaned of all the unnecessary data. So, the focus can be easily cast upon the main text. 
3. Text recognition: That is where all the important text in black against the white background is taken notice of. Each character is processed carefully, one at a time. Both types of recognition modes, pattern and feature, are utilized here.  
4. Layout recognition: Advanced OCR programs also analyze the layout of the uploaded images for better text processing! 
5. Postprocessing: That is the final stage where all the important extracted text data is stored in a single file.

The OCR technology is frequently used in data extraction processes, which are part of data ingestion and other key business analytics activities. 

Types of Optical Character Recognition Software

The optical character recognition software can be mainly divided into the following categories. It includes desktop OCR software, cloud-based OCR software, mobile OCR applications, AI-powered OCR software, etc. 

• Desktop OCR software: This is the program that is used after installation on the premises.  
• Cloud-based OCR software: Here, the user can access the program over the internet with a subscription. 
• Mobile OCR applications: Such software systems are even accessible on the mobile screen, and the available Android and iOS technologies. 
• AI-powered OCR solutions: Artificial intelligence is reaching its peak and covering all aspects of life these days. Thus, OCR software is also becoming AI-powered.  
• Enterprise OCR platforms: These are meant for the use of large-scale outsourcing companies.  

Do you know? All these categories slightly change the OCR meaning. That is due to the varying capabilities.

Also Read: What is Data Entry? Meaning, Roles, Salaries, and Job Types

Types of OCR Technology

After discussing “what is OCR?” and software types, let’s move on to types of OCR technologies. The following list includes simple OCR, optical mark recognition (OMR), intelligent character recognition (ICR), and intelligent word recognition. 

• Simple OCR: This is the simplest and most limited form of the OCR technology. Here, each character is recognized based on the pre-loaded data of characters. It is referred to as a pattern-matching algorithm. It is often used when you outsource data entry services.

  Do you know? Simple OCR matches character-related patterns, and that is why it is called optical character recognition. If it matches the given text word by word, it is called optical word recognition. 

• Optical mark recognition (OMR): This technology is for recognizing other crucial information on images. It could be checked boxes, survey bubbles, signatures on forms, logos, watermarks, etc. These are marks that the OMR software has to identify based on the pre-stored images.  
• Intelligent character recognition (ICR): Through continuous learning and the power of AI, programs can identify and thus read characters nearly like a human being. They start to take notice of certain particularities in a piece of writing over time and adapt themselves.   
• Intelligent word recognition (IWR): When ICR, as mentioned above, advances a level up, it becomes intelligent word recognition. 

Benefits of Optical Character Recognition

The key benefits of optical character recognition software are as follows. Using this software saves time, reduces manual data entry, improves accuracy, makes documents searchable, enhances productivity, and lowers operational costs.  

• Saves time: Manual entries take time and consume more resources than OCR solutions. The software with cutting-edge AI capabilities identifies characters and quickly creates machine-ready text documents. 
• Reduces manual data entry: Now, data owners don’t need to spend hours creating different spreadsheets. That is a real cost- and time-saving factor. 
• Improves accuracy: Human minds are always prone to errors. That is where the edge of using OCR comes in. AI and software don’t suffer from emotional turmoil and burnout.  
• Makes documents searchable: You cannot search for a particular textual bit of data in a database containing heaps of image texts. But once all the crucial text-based data is extracted from these images and then stored in text-only documents, everything becomes super discoverable.  
• Enhances productivity: Human errors are reduced, and the same task gets completed in less time. That increases the output level up to a considerable point. 
• Lowers operational costs: In-house data entry is comparatively more expensive than outsourced services. So, operational expenses also decrease. 

Common Applications of OCR

We have well understood, “What is optical recognition?” Now, let’s explore how this technology is streamlining workflows in various sectors and industries. Banking & finance services, healthcare, the legal industry, education, government agencies, and human resources are major areas where it is used.  

Banking and financial services	Most of the OCR tools are used here due to the vastness of transaction-related record generations on a daily basis.
Healthcare	It is one of the highly lucrative and data-rich sectors in any economy.
Legal industry	This industry entirely depends on a variety of contracts. Thus, OCR software helps make documents digital, searchable, and editable faster with efficiency.
Education	Now, handwritten documents can be easily converted into digital copies in a few simple steps.
Government agencies	A variety of text data on images can now be made searchable on screen.
Human resources	Regular receipts, employee-related hard-copy documents, and many types of other administrative data in image format are a real hassle without OCR-enabled digitalization.

Additionally, OCR software is also used in logistics & supply chain, retail & e-commerce, insurance, and accounting & tax documentation.  

OCR for Different File Types

You can use optical character recognition software tools with multiple file formats. It supports all types, including. 

• PDF documents 
• printed books 
• receipts 
• invoices 
• identity documents 
• business cards 
• handwritten notes 
• images, etc.  

You can feed all types of images into the system and then let it convert them into text-only documents. Your information, this way, becomes searchable and editable. That makes day-to-day data processing super easy.   

Also Read: What is Data Processing Outsourcing? Benefits, Trends, Common Services, & Much More!

OCR vs Other Recognition Technologies

This section compares optical character recognition with other technologies. Consider the key differences among OCR, OMR, ICR, Barcode Recognition, and Manual Data Entry below.  

1. OCR vs OMR

Optical character recognition differs from optical mark recognition in just one respect. The former deals with characters while the latter focuses on marks. Both use the same pattern-matching algorithm. 

2. OCR vs ICR

OCR and ICR both do the same job of converting images of text into text-only documents. But the difference lies in the technology they use. OCR uses a character-by-character pattern-matching algorithm. On the other hand, ICR utilizes cutting-edge AI and advanced machine learning models. It allows the software to read like a human being. 

3. OCR vs Barcode Recognition

The main difference between OCR and Barcode Recognition is twofold. It includes the nature of the material that is being converted into machine-readable information and the key processes involved. 

• Nature of the material: In OCR, characters on images are extracted and collected in a new, suitable document. However, in BR, the focus of conversion is a barcode.
• Key processes: In OCR, characters on images are first matched with the pre-loaded data (or characters). And, after matching each image character to a database character, conversion kicks in. But in the case of BR, the main task the software has is to decode the lines, spaces, and patterns in the barcode. That happens based on pre-loaded algorithms.  

4. OCR vs Manual Data Entry 

In OCR, the software handles the task of data entry, along with a certain level of human intervention. But in manual data entry, everything from A to Z is managed by a team of professional human beings. 

Limitations of Optical Character Recognition

You have known so many things so far, besides what is optical character reader! But analyse its restrictions as well. It is related to poor image quality, handwriting recognition challenges, complex layouts, low-resolution documents, etc. 

• Poor image quality: You need high-definition, clear images so that the software can scan and identify characters well. 
• Handwriting recognition challenges: If you have handwritten documents, the software might have difficulty recognizing characters.
• Complex layouts: If the text on the image is organized in a complex layout, i.e., multicolumn tables, that can give the tool a hard time getting its machine logic around. 
• Low-resolution documents: You might find the final output low in quality. Thus, to fix that, you have to use some other software to increase the clarity value. 
• Multiple languages: If your image input is in more than one language, that also creates a level of difficulty for the technology. AI-powered solutions might handle this well, but charge higher prices. 

Best Practices for Improving OCR Accuracy

For high accuracy in the output while using software character recognition, try the following simple rules. 

• Use high-resolution scans 
• Improve image contrast before uploading. 
• Align documents properly 
• Keep the software updated. 
• Review the extracted text thoroughly and check for potential errors.  

These simple tricks will provide you with quality results. 

Future of Optical Character Recognition

The future of OCR depends entirely on advancements in the field of AI, deep learning models, better mobile experience, robust cloud-based capabilities, and high-tech intelligent document processing (IDP). 

However, AI and machine learning both have already streamlined OCR processes to a great extent. Intelligent character recognition (ICR) is already nailing reading text at a near-human-like proficiency.   

Also Read: AI-Driven Data Entry Automation: Speeding Up Operations, Increasing Efficiency, and Boosting Performance

Conclusion

The optical character recognition technology is one of the most effective data entry and data extraction tools! As per your needs, there are multiple levels of capabilities available, such as simple OCR, OMR, ICR, and IWR. One enterprise might need all of them at once or one at a time, according to the nature of the work. This recognition software reduces operational costs significantly, boosting productivity in data processing!  

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