What is Optical Character Recognition (OCR): Overview and its applications

Optical Character Recognition might sound intense and foreign to most of us, but we have been using this advanced technology more often. We use this technology quite extensively, from translating the foreign text into a language of our preference to digitizing printed paper documents. Yet, OCR technology has advanced further and has become an integral part of our tech ecosystem.

However, there is much too little information about this innovative tech, and it is time we shine the light on it.

What is Optical Character Recognition (OCR)?

A part of the Artificial Intelligence family, Optical Character Recognition is the electronic conversion of text from handwritten notes, printed text from videosimages, and scanned documents into machine-readable and digital format.

It is possible to encode text from a printed document and electronically modify, store or alter it to be stored, recovered, and used for building ML models using OCR technology.

There are two basic types of OCR – the traditional and the handwritten. Although both work towards the same result, they differ in how they extract the information.

In traditional OCR, the text is extracted based on the available font styles that the OCR systems can be trained with. On the other hand, in a handwritten OCR, where each writing style is unique, it is a challenge to read and encode. Unlike typed text, where the text appears the same across the board, handwritten text is unique to the individual. Handwritten OCR needs more training for accurate pattern recognition.

How does OCR Technology Work?

There are three significant hardware and software elements involved in the working of OCR technology.

Step 1: Converting the Physical Document into Digital Image

In this phase, there is a need to have an optical scanner component to convert the document into a digital image. If the document is in a physical paper, it is essential to define the area of interest so that only those areas are subject to decoding. The areas with the text are considered for conversion while the rest remain null. The images on the document are converted into background colors while the text remains dark – this helps in separating the characters from the background.

Step 2: Character Recognition Phase

This step kick starts the process of recognizing specific characters in the text. The system doesn’t proceed to analyze the entire text – numbers and letters – at one go. It chooses smaller segments, most likely single words if the AI system can recognize the language accurately.

Feature recognition: It is used to identify the newer character with the help of rules that determine specific characteristics of the text. For example, the letter ‘T’ might look very simple to us, but it is a relatively complicated combination of vertical and horizontal lines for an AI.

Pattern Recognition: The AI is trained using a collection of texts and numbers to automatically identify and recognize matches from the documents to its learned repository.

Step 3: Processing and Output Text

All the identified characters are converted into ASCII code to be stored for the future. It is essential to have post-processing so that the first output can be double-checked. For example, the letters ‘I’ and ‘1’ might look a little similar, making it difficult for the system to recognize, especially when handwriting is involved.

High-quality Invoice / Receipt / Document Dataset to Train Your AI Model

Advantages of OCR

Advantages Of Ocr

Optical Character Recognition – OCR technology – brings a range of benefits, some of which are:

  • Increase the speed of the process:

    By quickly converting unstructured data into machine-readable and searchable information, the technology helps in increasing the speed of business processes.

  • Boosts accuracy:

    The risk of human errors is eliminated, which improves the overall accuracy of the character recognition.

  • Reduces processing costs:

    The Optical Character Recognition software is not entirely dependent on other technologies, reducing processing costs.

  • Enhances Productivity:

    Since information is readily available and searchable, employees have more time to do productive tasks and achieve goals.

  • Improves customer satisfaction:

    The availability of information in an easily searchable format ensures higher satisfaction levels and a better customer experience.

Use cases and applications

Preservation of documents / Digitization of Documents

Transcription Of Documents Old historical documents of value can be preserved, stored, and made indestructible by converting them into digitized format. OCR technology is being used for digitizing antique and rare books, so these manuscripts with irregular fonts can be digitally altered and made searchable for the future.

Banking and finances

The banking and finance sector is using the OCT technology to its hilt. This technology is helping improve security fraud prevention, reduce risk, and faster processing. Banks and banking apps use OCR to extract crucial data from checks such as the account number, amount, and hand signature. OCR is helping in the faster processing of loan and mortgage applications, invoices, and payslips.

Before OCR became more common, all banking documents such as records, receipts, statements, and checks were physical. With OCR digitization, banks and financial institutions can streamline processes, eliminate manual errors, and improve process efficiency by quickly accessing data.

Number-plate recognition

Number Plate Recognition Using Ocr The OCR technology is extensively used in identifying the numbers and text in number plates. This technology is being used in identifying lost cars, parking fee calculations, and preventing vehicular crimes.

OCR technology is helping implement road safety rules to avoid fraud and crimes. Since the number plates on a vehicle are linked to the driver’s credentials, identification is easier.

Moreover, the number plates consist of a well-written bunch of numbers and text that is not difficult for the AI model to read, making it easier and more accurate.


Text-to-speech application of OCR technology is an excellent help for visually-challenged people to function with greater ease. OCR technology helps in scanning physical and digital texts and using voice devices. The content is then read aloud. Although the text-to-speech aspect of OCR technology has been one of the first applications, it is now evolved and advanced to cater to the unique needs of visually challenged people by supporting several dialects and languages.

Transcription of Multi-category Scanned Paper Documents Datasets

Ocr – Multilingual Document 1 Using OCR technology, invoices, receipts, bills, and other documents of different categories are also transcribed effectively. Newsletters, papers with numbers in circles, checkbox forms, and documents with several categories such as tax forms and manuals can also be digitized.

Transcribe Medical Labels with OCR

Transcribe Medical Labels With Ocr By helping in scanning prescription medical labels using OCR, it is now possible to automatically capture medical data. The medical data is captured from handwritten prescriptions, drug information, and quantity to avoid manual errors, duplication, and negligence.

With OCR, the healthcare industry can quickly scan, store, and search for a patient’s medical history. The OCR makes it possible to digitize and store scan reports, treatment history, hospital records, insurance records, x-rays, and other documents. By digitizing, transcribing, and storing medical labels, OCR makes it easy to streamline the process flow and speed up healthcare.

Detecting Street/Road & Extract Information Street Board data with OCR

Detecting Street/Road &Amp; Extract Information Street Board Data With Ocr Automatic detection, identification, and classification of road/street signs are being made with OCR. By detecting road signs, OCR is directing drivers towards a safer journey. The OCR technology works equally well under low-light conditions, detects road signs in several languages and differently shaped signboards, and classifies the same for the future.

To develop an intelligent character recognition tool, you must train it with the project-specific dataset.

At Shaip, we provide a completely customized document dataset to develop highly-functional OCR for AI and ML models. Our specialized process of OCR helps in developing optimized solutions for clients.

We provide extensive and reliable datasets that contain thousands of diverse extracted data from scanned documents. Get in touch with our OCR solutions experts to know how we provide scalable, affordable, and client-specific datasets.

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