OCR in Document Processing Automation


Intro text

Optical Character Recognition (OCR) defines the process of mechanically or electronically converting scanned images of handwritten, typed, or printed text into machine-encoded text. OCR technology is an important first step in transforming analog data into digital data.

What is OCR?

Optical Character Recognition is a business technology that distinguishes printed or handwritten text characters from a scanned document of image files, which then examines and converts the characters of a document into code that can be used for data processing. OCR can also be referred to as text recognition.

OCR systems are a combination of both hardware and software solutions, where the software can deploy artificial intelligence (AI) to implement more advanced methods of intelligent data recognition, like identifying languages or different styles of handwriting.

How Does OCR Work?

When a physical form of a document is scanned, it is saved as a bit-mapped file then OCR software converts the document into a series of white and black dots.

The scanned image or bitmap is then analyzed for patterns and differences in light and dark areas. The dark areas are identified as characters that need to be recognized and the light areas are identified as background.

Once all the dark areas have been identified, they are then processed further to find alphabetic letters or numeric digits. OCR systems can vary in their techniques, but usually involve focusing on one character, word, or block of text at a time.

The characters are then identified using one of two algorithms:

Pattern Recognition: OCR systems are provided examples of text in various fonts and formats, which trains the system and are used to compare and recognize characters into scanned documents.

Feature Detection: OCR systems apply rules regarding the features of a specific number or letter to recognize character in the scanned document. Character features can include the number of angled lines, crossed lines or curves for comparison. For example, the letter “A” may be recognized as two diagonal lines that meet with a horizontal line across the middle.

How Is OCR used in Document Process Automation?

Optical character recognition (OCR) is an important feature in any document process automation solution. OCR technology is used to extract typed or handwritten text and images from documents, converting them into data that can then be used in BPA, without someone manually capturing it.

OCR is an older technology that originally need to be trained one font at a time with images of each character, but with modern OCR solutions, using AI, they are able to recognize and capture data from machine printed documents with high levels of accuracy.

Why OCR is Important?

OCR in business process automation enables organizations to automate a scalable volume of their business processes, especially areas that are still dependent on scanned paperwork such as written forms.

OCR can unlock the true power of contextual business information and enable organizations to improve productivity and efficiency. Partnering with a reliable OCR and business process automation platform is the best way to achieve this.

The result is highly accurate, fast, and high-quality data outputs, enabling businesses to automate more tasks, while reducing operating and training costs.

OCR Technology Use Cases

Many SMB and large enterprises need to automate and digitize their business processes, as well as take advantage of big data. One of the biggest challenges to this is the cost and complexity of storing and analyzing scanned documents. Most companies today still process large volumes of documents in hard copy formats with no digital copies available.

This is where OCR along with a digital process automation platform comes into play. Together, they can automate time-intensive manual processing tasks and seamlessly transition between fully automated and human supported inputs as needed.

A use case might be extracting data from a scanned invoice and inserting it into a accounting system. Text analytics can then be utilized to categorize the data, then automation will start and update the accounting system. In the event of errors, the system will alert a human to intervene.

Another use case could be identifying and verifying your customers when opening a bank account to make sure your customer is genuinely who they say they are. The OCR solution could verify the customers ID from their driver’s license and if there is a discrepancy, such as the name, it can be referred to a human verification. The system can also learn human behavior or input, enabling greater efficiency when managing similar events in the future.

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