The Rise of Optical Character Recognition in Business

Written by Kshema Sudhir

Optical Character Recognition (OCR) works as a text recognition tool to convert text in images, whether handwritten or typed, to machine-encoded text allowing for easier data entry and information processing procedure. The first OCR device was developed and patented in 1929 by an Austrian engineer, a complex device consisting of more than three components built to find and extract data from pictures. Following which there were several developments leading to the cloud-based OCR software of today, including Adobe Acrobat Pro DC, Abby FineReader, and Readiris.

Although the system has been in place for almost a century, the use of OCR in business has only risen recently due to the rising need to integrate advanced technologies with business processes to increase the efficiency of these processes. OCR has been used in business primarily to cut down productivity costs resulting from manual data entry and sorting of information, to increase the accuracy of data reflected and therefore, increase the quality of service provided to consumers and other key stakeholders. Using OCR for invoice and form automation has significantly reduced the time required to manually record data, and it also carries out an automatic validation to determine the accuracy of data contained, following which this data is analyzed and transmitted to related departments for further processing.

The OCR software used varies with different developers as well as its intended purpose, although its basic processing remains the same. The process usually initiates with an initial scan of the document to be parsed, in an attempt to make the contents clearer for further processing, by cleaning up the image and segregating characters from all other content. This is followed by the isolation of each character and the identification of spaces between characters, which leads to the next step of identifying the words represented by the characters found. Most OCR devices use either pattern recognition or feature detection to identify and determine words and sentences from the original image.

Pattern recognition is usually used to extract data from surveys and forms and relies on the usage of standard fonts that set out the characters contained and are therefore more suited for typed or box form images. Feature detection and Intelligent Character Recognition are more sophisticated tools where the software identifies letters based on specific rules that pick out the unique characteristics of each letter, and also uses surrounding features to ensure the accurate identification of words contained. Most OCR programs use feature detection, rather than pattern recognition, as it provides a higher rate of accurate extractions. Recent OCR devices have also started using neural networks to extract patterns in a more brain-like manner.

The benefits of implementing an OCR system in businesses are numerous. One of the key advantages identified was the increased accessibility experienced by customers using company databases to gather information where the customers can gather complete data by entering their names, thereby significantly reducing the time usually spent sifting through piles of paperwork. OCR allows firms to build comprehensive and searchable data by providing editable outputs from unstructured datasets which reduces the need for employees to do this work manually and therefore reduces the costs of extra personnel and allows all employees to work at maximum capacity. Companies have also experienced an increased customer retention rate as the use of OCR has increased the quality of services provided to clients, leading to increased customer loyalty owing to positive user experience.

However, despite the numerous advantages presented by the OCR system, some significant drawbacks need to be considered before relying completely on such a system. OCR’s ability to draw data from physical documents is limited to the type of font used, the system was found to return a higher rate of error when the font resembles handwriting or is a non-Latin font compared to clearer fonts like Arial or Times New Roman. In order to ensure 100% accuracy, OCR software will need to be paired with a clean-up tool to eliminate any errors arising from the initial data extraction. The possibility of errors also increases the requirement to manually check the data extracted which increases costs, both in terms of time and effort. Nonetheless, most of the drawbacks identified can be overcome by using additional or advanced OCR software which will also enhance the quality of data obtained.

In conclusion, the use of advanced OCR technology by organizations can streamline key processes and result in higher employee efficiency by allowing more time to focus on other major areas of the business. Researchers have found that entities using OCR for automated data entry process 25-60% more invoices than a firm using manual data entry systems. While there is still a long way to go to developing the optimum OCR device, it is still perceived to be fundamental to the increased productivity and efficient functioning of businesses.