What is Optical Character Recognition (OCR) and how does it work?


Introduction

Optical Character Recognition may sound intense and foreign to most of us, but we are increasingly relying on this advanced technology. We use this technology frequently, from translating foreign texts into our preferred language to digitising printed paper documents. Nonetheless, OCR technology has improved and has become an essential component of our technological ecology. However, there is much too little information available regarding this remarkable technology, and it is past time we shed some light on it.

What is OCR?

Optical Character Recognition, a subset of Artificial Intelligence, is the electronic conversion of text from handwritten notes, printed text from videos, photos, and scanned documents into machine-readable and digital format. Using OCR technology, it is possible to encode text from a printed document and electronically edit, save, or transform it so that it can be stored, recovered, and used to create ML models. OCR is classified into two types: traditional and handwritten. Although they both aim for the same result, they differ in how they obtain it.


Traditional OCR extracts text based on the available font types with which OCR systems can be trained. On the other hand, handwritten OCR is difficult to read and encode because each writing style is distinct. Unlike typed text, which looks the same everywhere, handwritten text is distinctive to the individual. For accurate pattern detection, handwritten OCR requires more training with Dataset For Machine Learning.

What is the Process of OCR Technology?

The operation of OCR technology involves three major hardware and software components.


Step 1: Create a digital image of the physical document

During this stage, an optical scanner component is required to turn the document into a digital image using image data collection. If the document is printed on paper, it is critical to designate the region of interest so that only those portions are decoded. The text-filled portions are considered for conversion, while the remainder is left blank. The visuals on the document are changed to background colors, but the text stays dark, allowing the characters to be distinguished from the background.


Step 2: Character Recognition

This phase begins the process of identifying specific characters in the text. The technology does not examine the entire text - numbers and letters - all at once. If the AI system can effectively detect the language, it chooses smaller pieces, most commonly single words.


Feature recognition: It is used to detect the newer character using criteria that specify certain textual properties. For example, the letter 'T' may appear easy to us, but to an AI, it is a relatively intricate arrangement of vertical and horizontal lines.


Pattern Recognition: Using a collection of words and numbers, the AI is trained to automatically find and recognize matches from the documents in its learnt repository.


Step 3: Text Processing and Output 

All recognized characters are transformed into ASCII code and saved for future use. Post-processing is required so that the initial output may be double-checked. For example, the letters 'I' and '1' may appear similar, making it difficult for the system to distinguish, particularly when handwriting is involved.



The Benefits of OCR

Optical Character Recognition (OCR) technology provides a number of advantages, including the following:


Increase the process's speed: The technology aids in the acceleration of corporate operations by swiftly turning unstructured data into machine-readable and searchable information.


Improves accuracy: Human errors are reduced, which enhances the overall accuracy of character identification.


Lowers processing costs: Because the Optical Character Recognition software does not rely fully on other technologies, processing expenses are reduced.


Increases Productivity: Employees have more time to conduct productive tasks and fulfil goals now that information is easily accessible and searchable.


Customer satisfaction is increased: Higher levels of satisfaction and a better customer experience are ensured by the availability of information in an easily searchable format.


Use cases and applications


Document Preservation / Document Digitization: By transferring old historical documents into a computerized format, they can be conserved, stored, and become indestructible. OCR technology is being used to digitize antique and rare books so that irregular font manuscripts can be digitally edited and made searchable in the future.


Banking and financial services: The banking and finance industries are making extensive use of OCT technology. This technology is assisting in the improvement of security fraud prevention, risk reduction, and speedier processing. OCR is used by banks and banking apps to extract critical data from checks, such as the account number, amount, and hand signature. OCR data collection speeds up the processing of loan and mortgage applications, invoicing, and paystubs. Prior to the advent of OCR, all banking documents, such as records, receipts, statements, and checks, were physical. Banks and financial institutions can use OCR digitalization to streamline procedures, minimize manual errors, and improve process efficiency by instantly accessing data.


Recognition of license plates: OCR technology is widely utilized in identifying numbers and text on license plates. This technology is used to find lost cars, calculate parking fees, and prevent vehicular crimes. OCR technology is assisting in the implementation of road safety rules in order to avoid fraud and crime. Identification is simplified because car license plates are connected to the driver's credentials. Furthermore, the number plates are composed of a well-written collection of numbers and language that is easy for the AI model to understand, making it easier and more accurate.


Text-to-speech: The text-to-speech use of OCR technology is a terrific way for visually impaired persons to function more easily. OCR technology aids in the scanning of physical and digital documents as well as the use of voice devices. The information is then read aloud. Although text-to-speech was one of the original applications of OCR training dataset, it has since progressed and advanced to meet the special needs of visually impaired persons by supporting multiple dialects and languages.


Datasets of transcribed multi-category scanned paper documents: Invoices, receipts, bills, and other papers of various sorts are also effectively transcribed using OCR technology. Newsletters, papers with numbers in circles, checkbox forms, and multi-category documents such as tax forms and manuals can all be digitized.


OCR is used to transcribe medical labels: It is now possible to automatically acquire medical data by assisting in the scanning of prescription medical labels using OCR. To eliminate manual errors, duplication, and carelessness, medical data is obtained from handwritten prescriptions, drug information, and amount. The healthcare business can use OCR to swiftly scan, store, and search for a patient's medical history. OCR enables the digitization and storage of scan results, treatment histories, hospital records, insurance records, x-rays, and other documents. OCR simplifies the process flow and speeds up healthcare by digitizing, transcribing, and storing medical labels.


Detecting Street/Road and Extracting Information from Street Board Data: OCR is used to automate the detection, identification, and classification of road/street signs. OCR directs vehicles to a safer journey by identifying road signs. The OCR technology identifies and classifies traffic signs in several languages and diverse shapes, and it works equally well in low-light circumstances.


You must train an intelligent character recognition tool with the project-specific dataset

Global Technology Solutions offers a fully customized document dataset for the development of highly functioning OCR for AI and ML models. Our customized OCR technique aids in the development of optimized solutions for clients. We offer vast and dependable datasets like consisting Video Dataset along with Data Annotation Services containing thousands of different extracted data from scanned papers. Contact our OCR solutions experts to learn more about how we provide scalable, economical, and client-specific datasets.

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