Optical Character Recognition (OCR) technology revolutionizes document management by enabling organizations to convert paper documents into a more secure, accessible, and usable digital format.
This article provides an easy-to-understand overview of optical character recognition–what it is, how it works, and why it’s such a powerful driver of digital transformation for companies across various industries.
Optical Character Recognition (sometimes referred to as Optical Character Reader) is a technology that converts printed or handwritten text into a digital format that analysts and general business users can edit, search, store, and display electronically.
Optical character recognition software uses algorithms and machine learning to analyze text characters’ shapes and patterns and then translate them into a format that computers can understand.
Companies can use OCR technology in various applications, such as converting scanned documents into editable text, digitizing books and archives, and processing data from forms and invoices.
OCR has traditionally been beneficial in healthcare, legal, finance, and government, where large volumes of documents must be processed and analyzed. But with the proliferation of documents and the power of data, OCR has become essential for every business. It’s useful for individuals too, which is why smartphone apps like Google Docs offer optical character recognition online for free.
The origins of optical recognition can be traced back to the turn of the 20th century when a series of ingenious inventions were designed to aid the visually impaired.
One such OCR device was the Optophone, which was invented in 1913 by Dr. Edmund Fournier d’Albe, an Irish astrophysicist and chemist. The Optophone used selenium photosensors to detect black print and convert it into an audible output, allowing blind individuals to interpret the written text through a series of unique chords.
The Optophone had limitations, however–even power users could only achieve a rate of 60 words per minute. Still, it laid the foundations for future devices like the Optacon, invented by Stanford professor John Linvill, which transformed words into vibrations felt on the fingertips. Later, companies used Optophone-like devices to digitize Reader’s Digest coupons and sort mail at the US Post Office.
By the 1970s, scanners were widely used for converting all manner of paper files–from price tags to passports–into digital format. By the early 2000s, products like Adobe Acrobat, WebOCR, and Google Drive made OCR technology available to the masses.
In today’s world, data-hungry technologies such as artificial intelligence and machine learning have resulted in an unprecedented surge in the need for electronic information. Simultaneously, companies are striving to digitize decades’ worth of paper documents into new document management systems.
These and other digital transformation initiatives have led to a significant expansion in the global OCR market, now valued at over $10.62 billion. Experts predict that the market will continue to grow at a compound annual growth rate (CAGR) of 14.8% from 2023 to 2030.
Once images have been acquired, the OCR process can be split into 3 phases: Preprocessing, Text Recognition, and Postprocessing.
During the preprocessing stage, scanned images undergo cleansing to prepare them for the next phase, Text Recognition. The primary cleansing technique used is binarization: Pixels below a specific threshold (representing dark areas) are marked as text and turned black. Those above the threshold are designated as the background and turned white.
Other preprocessing techniques include:
Text recognition analyzes the image or scanned document to identify characters such as letters, numbers, and symbols. Different OCR solutions use a combination of pattern recognition, artificial intelligence, and machine learning algorithms.
Traditionally, OCR has performed text recognition using either matrix matching or feature extraction. Matrix matching compares the pixels of an image to a stored template of characters. It works well for printed text but poorly for handwriting. Feature extraction uses more advanced algorithms to break down characters into smaller parts such as lines, loops, and intersections. It then compares these features to a database to find the best match.
Today, solutions like Google Docs use AI to recognize whole lines of text instead of single characters, improving efficiency and accuracy. Google’s Cloud Vision app can even detect text, handwriting, and objects in videos.
Other recent advances in the field of document analysis include Iterative OCR, a technique that involves processing text in multiple passes. On the first pass, the OCR software attempts to recognize the text (this is where traditional OCR stops.) In subsequent passes, the software identifies errors and inconsistencies and uses the results of the previous pass to improve accuracy further. This iterative process continues until the required level of accuracy is achieved.
Following Text Recognition, OCR converts extracted data into a computerized file. Here an additional layer of processing can improve consistency and accuracy. Text can be checked for spelling and grammar or constrained by a lexicon. Any words not in the Dictionary or on the list are marked as errors.
We’ve discussed the features, and now here are the benefits that make OCR an excellent investment for new businesses or existing companies undergoing digital transformation.
By integrating OCR, you can quickly scan and process paper documents before routing them to your document management system. The system can then use relevant metadata to index the documents, ensuring safe storage and quick access through its search function.
OCR unlocks valuable insights from unstructured data trapped in paper documents. It lets companies consolidate and analyze data to benchmark performance, pinpoint areas of inefficiency, and make faster strategic decisions.
OCR eliminates the need for manual data entry, which saves time and lowers labor costs. It also ensures high accuracy, reducing the risk of expensive mistakes that kill productivity and lead to poor customer experiences.
OCR technology lets you consolidate all your paper and electronic documents into a centralized repository, creating a single source of truth. This centralization, in turn, enables you to easily search and access high-quality, up-to-date data from all sources, improving cross-domain collaboration and driving innovation.
New technologies like artificial intelligence, big data analytics, and machine learning require access to vast data sets. OCR lets you amass large quantities of accurate data that you can use to fuel AI models and algorithms.
Regulators across most industries stipulate that companies store documents for a specific duration following a transaction. OCR allows companies to store documents in a digital rather than a physical archive. This digitization protects documents against loss and security breaches, reduces storage costs, and aids compliance.
Get in touch with DocStar to learn more on the value of DocStar’s Intelligent Data Capture.
