What Is Optical Character Recognition?

Optical character recognition (OCR) is a technology that recognizes text in a digital document, such as a picture or scanned document, by translating it into a machine-readable format. With hardware scanners and advanced software, OCR works by identifying letters and then rebuilding the letters into words and sentences using code. An everyday function of OCR is scanning receipts and business contracts.

Advances in this technology include using artificial intelligence (AI) to recognize different languages and handwriting. This allows for the conversion from analog to digital for many legal and historical documents. Explore the different types of OCR, use cases, advantages and challenges, and how you can start using it. 

Types of OCR technology

You can separate OCR technology into types based on the complexity of the software models and whether they use AI to interpret the characters. Essentially, you categorize OCR technology depending on what it can capture. 

The four main types of OCR include:

• Simple OCR

• Optical mark recognition (OMR)

• Intelligent character recognition (ICR)

• Intelligent word recognition

Below, you can further explore each type of OCR technology and the different items they can capture. 

1. Simple OCR

Simple optical character recognition is a pattern-based OCR system that matches patterns of characters using an algorithm linked to a database to match characters based on its pattern templates. These systems have limitations since only a certain number of fonts, languages, and handwriting styles can fit into the database. Some simple OCR systems match word by word instead of character by character, also known as optical word recognition. 

2. Optical mark recognition (OMR)

Since documents contain more than just letters, you need technology like OMR that can interpret everything else. OMR captures and analyzes items on a document, like watermarks, logos, patterns, and symbols.

3. Intelligent character recognition (ICR)

Using advancements in machine learning, deep learning, and AI, ICR attempts to mimic reading the same way your brain does—practice, practice, and more practice. Using neural networks, it works through iterations to decipher each letter geometrically. ICR analyzes every loop, curve, and line that make up characters through iterative patterns, tracing over each multiple times before producing a result. 

4. Intelligent word recognition

Intelligent word recognition builds on the same methods of ICR, using machine learning and deep learning, but it attempts to read like humans at the word level instead of the character level. Similar to how you would read by looking at a word in its entirety, intelligent word recognition studies the entire word as an image. 

How does OCR work?

Optical character recognition works through a series of steps, which take a scan of the original document and turn it into a digital file or PDF that you can edit. 

These steps are as follows:

1. Image acquisition: A hardware scanner turns the physical document into an image that contains binary data. The software creates a bitmap based on the dark and light portions of the image, which then sees dark as needing further processing and light as background. 

2. Preprocessing: OCR software then manipulates aspects of the image to ensure better text recognition, such as realigning off-skew aspects, removing unneeded graphics or boxes, and identifying any script text. 

3. Text recognition: To identify the characters, the system typically utilizes one of two algorithms: pattern recognition or feature recognition. With pattern recognition, the OCR tries to match the characters to a font and language within the system’s collection of template patterns. Regarding feature recognition, the OCR breaks down the lines, curves, and loops of the characters to identify the text. 

4. Postprocessing: After identification, the program puts the data into a new digital file that you can edit further or into a PDF. Be sure to cross-check your new file with the scanned version to fix any errors that may have occurred during processing. 

What is optical character recognition used for?

OCR's most basic application is converting analog print media into machine-readable digital files. It has many applications in various industries, such as health care and logistics, as well as everyday applications for users, and it can even assist visually impaired persons. 

Other applications of OCR include:

• Archiving historical documents into an indexable format

• Automating data entry, processing, and extraction from physical documents

• Using a mobile app to deposit checks into your bank

• Storing and scanning of critical physical legal documents in an archived, digital form

• Scanning and translating physical words into a different language

Who uses OCR?

OCR has applications in many industries, including health care, finance, and logistics. Many organizations benefit from using OCR for data mining, entry, and processing as the first step in a big data workflow. Learn more about its applications in the below industries:

• Health care: OCR helps scan patient records, tests, and insurance information. It allows patients to submit insurance claims with their phones and allows doctors to search digital medical records in seconds, compared to paper records. 

• Finance: In banking, OCR scans loan documents and allows for the mobile deposit of your checks by scanning them, making verification more secure. 

• Logistics: OCR serves logistics by scanning and processing invoices, labels, mail, and receipts for more efficient and accurate entry. 

Advantages and challenges of using OCR

OCR technology has many advantages, such as efficient scanning and processing of physical documents. However, it also has some disadvantages regarding quality, formatting, and complex character recognition. Discover some more advantages and challenges of OCR below. 

Advantages of OCR

• Scanning physical documents creates searchable PDFs and databases for businesses to make use of data analysis.

• OCR streamlines data entry workflows.

• OCR digitally secures important physical data.

• OCR improves access to documents for employees across an entire business.

Challenges of OCR

• The accuracy of OCR output depends on the quality of the original document, including complex fonts or blurry scans.

• Many OCR tools fail to keep the proper formatting of the original document, although some specialized software is overcoming this.

• OCR systems struggle with colored paper backgrounds since they depend on the contrast between the text and background.

• Using OCR comes with data and security risks as these systems digitize and store your personal information. 

How to get started in OCR

You can use OCR with tools like Adobe Acrobat Pro, which transforms documents into searchable PDFs. Other software options for you to begin employing this technology are OmniPage Ultimate, Abbyy FineReader, and Readiris. Computer vision, natural language processing, and deep learning are advancing OCR technology. Whether you work in retail, tourism, or insurance, your business can benefit from learning to utilize this technology effectively. If you work in programming, you can start building skills with OCR using Python and the OCR engine, Tesseract, to extract text from a scanned document. Using the two technologies together is called Pytesseract. 

To work in software development, you typically need at least a bachelor’s degree in one of the following subjects:

• Computer and information technology

• Computer science

• Software engineering

• Mathematics

• Software development

Suppose you’re interested in working as an OCR developer. In that case, you need to learn various skills, such as:

• Applying programming languages

• Comprehending data structures and algorithms

• Understanding databases

• Being comfortable with version control systems

• Having a solid grasp on testing and debugging software. 

Learn more on Coursera

Optical character recognition is an advanced technology that extracts text from physical documents or images into digital ones. If you're looking to learn more about this tool and data analytics, consider Generative AI for Data Analysts Specialization. This program may enhance your career as a data analyst by allowing you to gain knowledge of generative AI and applicable skills for data analytics.