In this article, we will explore the main differences and distinct qualities of OCR and Text Recognition. Read on to learn more.
Optical Character Recognition (OCR) is a technology that transforms various types of documents, such as scanned paper documents or images, into text that can be edited and searched. It allows computers to identify and process both printed and handwritten text from these sources.
Example: Using Adobe Acrobat Pro, OCR can convert a scanned contract into editable text, allowing you to quickly update clauses or search for specific terms within the document.
Here are some of the distinct qualities of an OCR:
OCR can convert printed or handwritten text from images or scanned documents into editable digital text.
OCR systems can recognize multiple languages, allowing for the processing of documents in various languages without needing separate tools.
Modern OCR technology offers high accuracy in recognizing characters, even from low-quality scans or images, minimizing the need for manual corrections.
OCR can identify and process different fonts, styles, and formatting within documents, maintaining the original layout.
OCR tools often support the bulk processing of multiple documents or images, increasing efficiency in large-scale document digitization.
Once text is converted, OCR makes documents searchable, enabling quick keyword searches within large volumes of text.
Text recognition is a technology that identifies and extracts text from images, printed documents, or handwriting. It focuses on accurately capturing the content without necessarily converting it into editable text.
Example: Using Google Lens, text recognition can capture the content of a handwritten note by extracting the text from an image, allowing you to copy and share it without needing to type it out manually.
Here are some of the distinct qualities of Text Recognition:
OCR excels at extracting individual characters from scanned images or printed documents, converting them into machine-readable text. This allows for easy editing and manipulation of the text within digital formats.
OCR systems are capable of recognizing and processing text in multiple languages, making them versatile tools for global applications. They can identify different scripts and alphabets, adapting to various linguistic nuances.
OCR not only converts text but also preserves the original layout and formatting of the document. This includes maintaining the position of columns, images, and other elements, ensuring the digital version closely resembles the original.
Advanced OCR technologies achieve high levels of accuracy, even with complex fonts, varying font sizes, and degraded image quality. This reduces the need for manual correction and enhances the reliability of the converted text.
Some OCR systems are equipped with the ability to recognize and digitize handwritten text, making them useful for digitizing notes, forms, and other handwritten materials.
OCR tools are designed to handle large volumes of documents quickly, making them suitable for enterprises needing to digitize extensive archives. Batch processing capabilities allow for efficient handling of multiple files simultaneously.
After text conversion, OCR enables the creation of searchable digital documents. This makes it easy to locate specific information within large datasets, improving the accessibility and utility of digital archives.
OCR and Text Recognition have differences and distinct qualities. We will explore these differences below.
We hope that our OCR vs text recognition article has now left you with a better understanding of the main differences between OCR and text recognition. If you enjoyed this article, you might also like our article on OCR software for data entry or our article on OCR data capture.