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What Is OCR?

OCR is an abbreviation for Optical Character Recognition or Optical Character Reader, a technology that recognizes the characters in an image captured by a camera or scanner and converts them into text data that can be recognized by a computer.

Since even handwritten text is converted into text data by OCR, once captured, the desired document can be immediately accessed by searching for it later. OCR can be done using a physical OCR scanner or by a cloud-based service that performs OCR on images prepared by the user.

Uses of OCR

OCR is often used to digitize documents, especially handwritten documents, to make them paperless and improve document accessibility. Today, many processes are managed online, but paper documents are still a common part of life.

Examples include receipts, handwritten notes, and surveys conducted at events and on the street. Paper documents are bulky and time-consuming to search through. Until now, turning those documents into text data required manual human input.

However, with the introduction of OCR, slips, and receipts can simply be scanned and digitized, converting them into searchable, editable data, resulting in much more efficient business operations.

Principle of OCR

After capturing an image, OCR performs three major processes for character recognition:

1. A process called layout analysis roughly separates the textual and non-textual parts of the image.
2. The layout analysis process then identifies columns and rows based on the extracted text.
3. Character recognition is performed by extracting individual characters from the columns and rows.

To identify the extracted characters, three additional processes are performed:

1. Character size normalization is performed to ensure the generation of uniformly sized characters.
2. Character features are quantified by reinterpreting a character as a set of line segments and deconstructing each segment into its directional components.
3. The character is compared to a pre-registered template and identified through pattern matching.

The metric used in process 3 is determined by calculating the Euclidean distance. Euclidean distance is the distance between two points as measured by a ruler and obtained by the Pythagorean theorem.

Recently, there have been many efforts to improve literacy rates by incorporating machine learning into the final matching process.

Types of OCR Software

In recent years, OCR has been offered in a variety of non-traditional forms. For example, OCR provided as a cloud service does not require software installation, and text data can be obtained by sending image files to the cloud service.

In addition, OCR facilitated by a smartphone application can convert images captured with a smartphone camera into text in real-time. Services that can read text with OCR and then translate it, or read receipts and automatically create household accounting records, have also emerged.

In many cases, OCR software is available free of charge for individual or small-scale use, and paid services can be tested on a trial basis.

Other Information on OCR

AI-Based OCR

In recent years, AI-based OCR, also known as AI OCR, has become increasingly popular and is being used by an increasing number of companies to digitize large volumes of documents.

Compared to conventional OCR, AI OCR is characterized by its ability to recognize text with higher accuracy through the use of machine learning techniques. If the text is easy to read, such as printed text, it can be read with nearly 100% accuracy.

In addition, with conventional OCR, it is necessary to define the bounding box and target content before scanning. With AI OCR, however, the AI automatically identifies the content that needs to be interpreted, eliminating the need for pre-design work. This makes it possible to read a wide variety of documents with ease.

Recently, Robotic Process Automation (RPA), has been gaining attention as a tool for automating document digitization. By implementing AI OCR and using RPA to automate tasks, the formerly tedious process can be simplified significantly.