What is Optical Character Recognition (OCR)?
May 03, 2025
14 min read
May 03, 2025
14 min read
Accurate business data is becoming more and more in demand every day. OCR technology allows any organization to access vital information without causing delays in workflows. Optical character recognition (OCR) is also referred to as text recognition. OCR speeds up the converting process and creates files that are more applicable.
OCR is a collection of technologies and methods that use computer vision and artificial intelligence to automatically recognize and extract text from unstructured documents, such as pictures, screenshots, and actual paper documents, with a high level of accuracy.
Hardware and software are combined in OCR systems to transform printed documents into machine-readable text. Software usually performs the sophisticated processing after hardware, like an optical scanner or specialized circuit board, copies or reads the text.
In order to identify languages or handwriting, OCR software can use artificial intelligence (AI) to apply more powerful intelligent character recognition (ICR) techniques.
Ray Kurzweil created OCR (Optical Character Recognition) in 1974, a technology that could read printed text in nearly any typeface. He built a device that reads aloud from text to assist the blind. In the 1990s, the technology began to be used to digitize old newspapers after Kurzweil sold his business to Xerox in 1980.
OCR has advanced over time to produce almost flawless text conversions. Processing documents without retyping them is made simpler and quicker, which saves time and minimizes errors. OCR is widely accessible today and benefits people in both personal and professional contexts.
OCR software applications may operate differently, but they all follow a few common rules. OCR technology typically follows a step-by-step process that includes:
Paper documents are read by a scanner, which then creates a scanned image of the document. Typically, the file is rendered in black and white, which is then used to distinguish between the darker and brighter areas. It is important for optical character recognition (OCR) to accurately convert the scanned image into editable text.
In order to increase the accuracy of scanned images, the OCR engine fixes errors using techniques like de-skewing, normalization, zoning, and binarization.
Here, scanned images or documents can be used to identify original characters using artificial intelligence (AI) tools. The two primary algorithms for doing this are feature extraction and pattern matching.
The extracted data is then converted to electronic documents using the OCR software. Modern OCR systems can guarantee optimal accuracy by comparing extracted data with a glossary or character library.
Data scientists use application and use to categorize various OCR technology types. Here are a couple of examples:
A basic OCR engine uses templates that store a wide variety of font and text image patterns. The OCR software compares text images to its internal database character by character using pattern-matching algorithms. It's known as optical word recognition if the system can match the text word for word. The inability to capture and store every type in the database and the nearly infinite variety of font and handwriting styles make this solution limited.
Modern OCR systems read text using intelligent character recognition (ICR) technology, just as humans do. They employ advanced techniques that use machine learning software to teach machines to behave like people. A machine learning system known as a neural network processes the image repeatedly while analyzing the text on multiple levels.
It looks for curves, lines, intersections, and loops, among other image characteristics. The final result is obtained by combining the findings from all of these levels of analysis. ICR is quick, producing results in a matter of seconds, despite the fact that it usually processes the images one character at a time.
Similar to ICR, intelligent word recognition systems process images of entire words rather than first converting them into characters. Optical mark recognition can recognize text symbols in a document including watermarks and logos.
OCR technology offers the following benefits:
Data security: OCR technology helps companies to comply with regulations and reduce threats to their data security. It protects your information and reduces the likelihood of fraud, document alteration, and tampering.
Improve Productivity: Businesses can increase productivity by using OCR to quickly retrieve data. Employees can now focus on other business areas with the time and effort they once spent manually retrieving any particular data.
Cost Reduction: OCR can assist companies in cutting expenses related to processing, handling and storing paper documents.
Reduce errors: The software provides its users with a stress-free experience because there are very few chances of errors occurring during use. OCR makes it easier and more efficient to complete these tasks more quickly while maintaining accuracy and precision.
Stored data efficiently: The full text-search ability of OCR-processed data allows staff members to swiftly and precisely look up specific information like names, addresses, dates, or other details.
The OCR (Optical Character Recognition) technology is an influential feature when it comes to text translation from images. The process involves scanning the image to identify regions of text. The system then decomposes the text into single characters and matches them against a database of symbols and fonts. New OCR employs deep learning to increasingly accurately recognize text, even in other languages, other fonts, or handwriting.
Once the text is pulled, it's translated into a digital format. Software translation, such as Photo Translator, can then translate the text into another language. The combination of OCR and translation improves processes like document digitization and accessibility by making text in images more readable and translatable.
OCR systems now achieve better performance through the implementation of deep learning and machine learning technologies. Through these technologies OCR systems can learn from large databases which increase their capability to identify different font styles and written languages and handwritten variations.
Low-quality images with noise become easier to process through machine learning algorithms which also recognize text even when documents have severe distortions. The development of OCR technology experienced major advances because of Deep learning architectures named Convolutional Neural Networks (CNNs).
Real-time OCR systems have benefited greatly from the addition of Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) as specialized hardware. Deep learning model training and inference processes receive tremendous speed-up from GPUs because these computational devices were built to handle parallel operations. TPUs manufactured by Google function better than other solutions to carry out deep learning operations. The technology enables real-time OCR systems to handle massive data processing at a fast speed.
Cloud computing has emerged as a prime factor that influences OCR technology development. Oakton Document Control Systems accommodate large data processing and storage through cloud-based OCR solutions to support wide-scale OCR system deployments. APIs allow Google Cloud together with Microsoft Azure and Amazon Web Services (AWS) to provide built-in OCR features which enable organizations to use OCR systems without needing proprietary hardware.
OCR systems benefit from the Internet of Things (IoT) through newly available possibilities. Smart devices ranging from wearables to sensors and smart cameras enable OCR systems to perform real-time image and video capture through the Internet of Things. OCR's integration with the Internet of Things provides a constant flow of information between devices and systems. It benefits users by offering real-time updates and decision-making.
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