Businesses are using OCR technology to recognize handwritten text and convert it into formats that are machine-readable. OCR can convert any code made up of letters and numbers, including serial numbers, into a digital output. Optical character recognition employs many methods. Simply said, after the input image has been analyzed, the characters are retrieved and detected. OCR scanning just looks at the characters you want to convert to a digital format; it ignores the nature of the object being scanned. Whenever you scan a word, for instance, it learns and identifies the letters but not the meaning.
Optical Character Recognition Procedure
The OCR procedure entails three fundamental steps, which are as follows:
Pre-Processing of Images
To improve the chances of successful recognition, OCR technology is used to pre-process the images. The primary objective of pre-processing an image is to enhance the image’s actual data. So, the unique feature of OCR technology is that it enhances an image, while all distortions are suppressed. It is necessary for the following steps.
Character Recognition in Documents
To recognize the character, one must understand what is meant by a “feature extraction.” A large set of data is processed only by choosing the important features and ignoring the unnecessary ones. Using a smaller collection of data instead of the first large one improves performance.
Post-Processing on Scanned Images
Post-processing is another error-correcting method that improves OCR accuracy. If the output is limited in the database, the accuracy could be enhanced even more.
As a result, the algorithm can revert to using a list of terms that are eligible to appear in a scan page.
Along with recognizing the right words, the text recognition software can read codes and numbers. This is helpful for deciphering long strings of characters and numbers, like serial numbers, which are utilized in numerous industries.
To respond to different types of input OCR more effectively, several providers started creating specialist optical character scanners. These algorithms can handle odd photos, and they have used a variety of optimization techniques to further increase recognition accuracy.
They employ formal language, common phrases, and rich details in a colored image, for example. OCR scanning software, also referred to as “custom OCR,” is a methodology that combines several optimization algorithms. Applications that use it include those that use invoice OCR, ID card OCR, and business card OCR.
Applications of OCR
Since OCR scanning works along with many other technologies, optical character recognition has a wide range of applications. Following are a few typical uses for OCR text recognition:
OCR Scanning in Food Industry
The food industry can create a digital menu using machine learning algorithms and OCR features. As well as a database of multiple recipes with calorie information and their apt intake amounts. In this way, the OCR feature improves both the customer experience and gives the food industry inspiration for new, creative processes.
OCR Scanning in Travel Industry
Users can travel more easily using OCR technologies, which advance the security and data-storage processes for passport verification and travel applications. OCR also makes data processing faster than human beings while minimizing manual errors in data verification. It has the potential to reimagine the travel industry and customer experience by using OCR technology in the booking and checking processes.
OCR Scanning in Legal Sector
Few businesses produce as much documentation as the legal sector, so OCR has several uses in this context. Using the most basic OCR scanners, hundreds and hundreds of affidavits, decisions, filings, statements, wills, and other official documents printed out can be digitized, stored, and made searchable. Due to OCR scanning technology, businesses can find documents written in any language in this digital world. Fast access to legal records from millions of prior cases is definitely a place for a sector that primarily relies on legal decisions.
OCR Scanning in Logistics
Logistics companies spend most of their time inputting logistics documents, working on engineering training modules, designing new machines, and developing new processes. OCR simplifies the process by allowing us to check input data autonomously during loading, thereby eliminating the possibility of error.
Almost every organization in the world has to deal with information processing and mining. For online businesses, processing huge amounts of customer information can be challenging. If the process uses a manual method, it is not only time-consuming but also expensive because the business needs to hire staff to process the data. OCR scanning is the greatest solution to this problem. Not only does it eliminate the need to pay specialists, but when combined with artificial intelligence (AI), it also rapidly converts paper-based documents into electronic PDFs.