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Most businesses deal with large volumes of printed stuff, like paper forms, contracts, invoices, and legal documents. Managing all that paperwork takes up a lot of time and storage space. Though companies try to opt for paperless documentation management, scanning documents into image files doesn’t fully solve the problem. This is because the text processing software cannot process and edit the text hidden in scanned images the same way as it can in text documents.
OCR technology overcomes this problem by converting text images into text data. Optical character recognition (OCR), mainly referred to as text recognition, uses advanced algorithms to analyze the pixels in an image and recognize the characters. It then extracts the texts from scanned documents and files. This article further explains how OCR works and how it can benefit various industries.
How Optical Character Recognition Works
OCR tools like OCR Studio use techniques like pattern recognition and image processing to convert the image into usable text. Following is the breakdown of each step:
- Image Acquisition and Preprocessing
The first step is to scan the document, and the scanner then converts it into binary data, in the form of 0s and 1s. The OCR software inspects the scanned file and differentiates lighter areas from darker ones, classifying the white part of the document as background and the dark as text. It then removes errors and cleans the image to prepare it for reading by using the following techniques:
- Cleaning up boxes and lines in the image.
- Slightly tilt or deskew a scanned document to fix alignment issues during the scan.
- Script recognition for multi-language OCR technology
- Text Recognition
After cleaning up the image, OCR software helps recognize the actual text through two methods: pattern matching and feature extraction.
- Feature extraction: Instead of trying to match the whole character, the software breaks it down into smaller pieces, like lines, curves, loops, and intersections. It then analyzes these features and compares them to similar ones in its database to figure out the best match. This method is better at handling different fonts, sizes, or even slightly messy text.
- Pattern matching: The software picks out a character from the image known as a glyph, and compares it to a library of known characters that it has already stored. If the scanned text is in a familiar font and size, this approach works well. It’s most effective with printed documents that use standard typefaces.
Use of OCR
OCR technology has been transformative across various industries. The global OCR market is estimated to reach $39.7 billion by 2030, growing at a steady 16% CAGR (Compound Annual Growth Rate) from 2022 to 2030. Different industries are using this technology to perform their daily operations to save time and costs, while enhancing productivity. The following are some of the common industries that can highly benefit from OCR:
- Banking: OCR is used to verify checks, loan papers, and transactions, cutting down fraud and speeding up processes.
- Healthcare: It helps digitize patient records, reduce manual work, and keep data organized and up to date.
- Logistics: OCR automates reading invoices and labels, improving accuracy and saving time on data entry.
- Travel: Speeds up passport and ticket processing by eliminating manual errors and making authentication faster.
- Government: OCR simplifies ID and license verification by scanning documents and accessing user data quickly.
- Food industry: OCR uses machine learning to help the food industry digitize menus and recipe databases with calorie information, improving customer experience and daily operations.
- Retail: Extracts data from bills and orders, organizing it for quick access and streamlining retail operations.
Endnote
OCR technology has been a game changer in helping industries process large amounts of data efficiently. By streamlining workflows and reducing the possibility of errors, it has helped businesses provide a better experience for customers and employees.
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