What Is OCR? A Comprehensive Guide to OCR Technology

Suppose your boss wanted you to digitalize a business report or some other business data. You could spend hours or days converting and correcting these physical documents into digital formats. 

Or better you could spend only a few minutes to get everything on your computer, laptop, tablet, or whatever device you are using at that moment. 

Yes, you heard that right! You can do this by using Optical Character Recognition software.

So, what exactly OCR is?

OCR stands for Optical Character Recognition. OCR is a technology that allows you to convert text into machine-readable data. OCR is a vital and well-known part of artificial intelligence that offers human-level recognition and converts data from printed texts from images, videos, scanned documents, and handwritten notes. 

The OCR online converts the text into special codes and electronically modifies the output so that it can be used for storing, retrieving, or editing physical texts. 

OCR comes in several forms, but traditional and handwritten OCR are the most common. They both aim for the same output, but their methods differ when it comes to gathering information.

When text is extracted using traditional OCR, the system uses the available font styles as training data. On the other hand, it is challenging to read and decode handwritten OCR, because each writing style is unique. To detect handwritten with equal accuracy, OCR needs to be improved through more training.

How does OCR Technology Work?

Here is the breakdown of how three considerable hardware and software elements contribute to the working of OCR technology.

Step 1: Creating a digital image of the physical document

The first step is to create an image from the physical documents you want to extract the text from. The image may come from any source, such as scanned documents or images captured with your smartphone camera. Image recognition is usually done using an optical scanner. The optical scanner recognizes the areas with text and marks them black, while the empty areas remain white or null. The black-and-white color gradient separates the characters from the empty background. 

Step 2: Recognition of characters

When the image text is separated from the background, it’s time to identify the characters contained within it for extraction. This process is known as character recognition and text extraction respectively. 

This is probably the important step in OCR processing that breaks down the text into smaller segments – probably in single words. Here the AI recognizes the language and text by following two steps. 

Feature recognition: this step involves character identification such as straight lines, curves, angles, and intersections. It is done by splitting up each character into specific features. As an example, the letter ‘L’ is easy to read and a singular character for us, but for OCR software, it is more like a horizontal or vertical line. 

Pattern recognition: To perform pattern recognition, each character is examined as a whole and compared to a matrix of characters stored within the software. Because this method relies on similarly shaped and sized input and stored characters, it may fail to recognize the text with 100 percent accuracy. 

Step 3: Output text processing

Finally, once all of these steps have been completed successfully, the identified characters will be translated into ASCII code for future storage. A post-processing step is necessary to ensure that the first output is double-checked. 

For example, the letters ‘I’ and ‘1’ resemble each other, complicating the recognition process for the software, especially when it comes to handwriting. You should also remove any unnecessary characters that are not part of the original document content (for example spaces and punctuation marks) so that your system does not get overloaded with extraneous information at some point during its operation.

Some common applications of OCR

OCR technology is a very effective way of converting scanned documents into digital files. OCR technology has become a valuable tool for capturing and analyzing analog data for both individual and business purposes.  There are a number of applications for OCR today, including

• Editing and managing data. Since more and more businesses are going paperless due to higher expenses on paper-based documentation, sustainability concerns, and higher error risks, OCR is an ideal bet. OCR tool converts all the paper data into digital formats automatically to be stored, edited, or retrieved for future use. This plays an important role in boosting the workflows. 

• Mobile applications. It’s hard to imagine a world without smart devices like phones, tablets, and laptops. The increased use of smart devices has made it possible to find the OCR tool in pockets and purses. This is because of the plethora of OCR mobile applications available out there. 

• License plate scanning. Detecting the license plates of automobiles has become more convenient and easier with AI-powered OCR software. This is especially popular among parking control staff and police forces as a way to track vehicles and deter criminal activity.

• Assessment of academic performance. OCR helps editors, reviewers, and professors to streamline their assessment process by offering quick and advanced paper analysis. 

• Process automatization. Manual data entry and document scanning are tedious jobs. With OCR technology in hand, you are able to do this challenging job in a matter of seconds – allowing you to cut labor and resources and speed up workflows.

Key takeaways

Optical Character Recognition (OCR) technology converts scanned documents into editable text files. This allows you to search and manipulate your document in an easy way without having to manually type in each word or letter. 

OCR is a perfect solution for converting scanned documents into searchable text files, which then can be used in word processors or other programs that allow you to search through your document files. 

Using OCR solutions, users can gain quick access to information. It Increases operational efficiency and processes by minimizing the time and resources needed to maintain hard-to-find or non-searchable data.