Optical Character Recognition(OCR)

Maintaining physical records can seem tedious in today's digital world, where every document is available on-screen. It can be time-consuming and challenging to manually enter all physically existing data into the system. Optical Character Recognition(OCR) makes work simple by picking up heavy data from images and converting it into e-docs to reduce your headache of manual data entry. OCR is a computer vision-based technology that efficiently digitalizes the physical copy of documents into machine-encoded text. With the advancement in computer vision, it is constantly improving, and now it is competing with human eyes by accurately extracting data from high-quality data images and pdfs. OCR helps AI enable machines to search the content quickly and locate the required information. Just think about when buying a foreign product covered in their regional language; OCR provides you with the most innovative solution for this. Just open your webcam, and OCR will extract the text, and AI will convert it into your language, simple right! Whether your document is handwritten or printed, OCR can convert it into digital format.

What is Optical Character Recognition (OCR)?

OCR is a technology that electronically identifies and analyzes the text written inside an image or document and converts it into a machine-readable text form/searchable form. One of the most used examples of OCR is Google's image search function that enables you to search documents based on the text present in your image. OCR plays a significant role in improving productivity and readability for departments dealing with many printed or handwritten documents. By processing files, OCR makes text ready for editing, searching, indexing, and retrieval. 

Impact on Businesses

Companies are incorporating OCR and AI into their operations and processes, consequently, fewer human errors. OCR enables businesses to operate with a fraction of their current workforce and increases production by re-deploying their valuable human capital to more efficient work. Now employees invest their time doing a more sophisticated task that AI cannot do. In addition, OCR positively impacts brands by increasing their data efficiency and effectiveness. 

How does Optical Character Recognition work?

The workflow of OCR is relatively easy for printed documents, but it becomes challenging for non-digital handwriting samples due to the variety of fonts or the methods used for letter formation. Therefore, the entire implementation is a three-step process.

  • Pre-processing of the image
  • Character recognition
  • Post-processing specific output.

Let explore how these steps are implemented

Scanning the Document

Scanning the document is the prime step of pre-processing the input. It connects the device with an Optical Scanner and creates a standardized document. This step improves the efficiency of the inputted document by ensuring perfect sizing and alignment of the document.  

Refining the Image

As the name suggests, the optical character recognition software enhances the document's elements that need to be captured in image refining. AI helps provide a plan and clear text by smoothing the edges and pixels of the image and removing the dust particles. In addition, it offers clear visibility to the program so that the program captures only required words and ignores unnecessary patterns.


Binarization plays a significant role in the optimal recognition of characters. It takes the refined image as an input and converts it into a bi-level document image. The bi-level image consists of only two colors, black and white, where color signifies the image's background and identifies as characters. So, binarization acts as a separator that differentiates the foreground text from the background by applying segmentation to the document.

Recognizing the Characters

Now we'll focus on the black area of the image to recognize characters, letters, or digits. Generally, an OCR processes one character or block of letters at a time. Characters recognition uses the following algorithms

  • Feature detection- To identify characters in the scanned document, OCR software applies rules considering the features of a specific letter through this feature detection algorithm. Features of a text can include the number of curves, crossed lines, angled lines, etc.
  • Pattern recognition- Through pattern recognition algorithm, OCR software involves inserting text in various fonts and formats into the OCR software for recognizing the characters in the scanned document.

Verifying the Accuracy

OCR software uses its internal dictionaries to check how accurate the recognized text is and measure accuracy by taking the output of an analysis conducted by an OCR and comparing it to the contents of the real version. However, there are two procedures for analyzing the accuracy of OCR software:

  • Character-level accuracy- counts how many characters were correctly identified.
  • Word-level accuracy- counts how many words were correctly identified.

The acceptable accuracy rate is around 98-99%, meaning that in a page of approximately 1,000 characters, 980-990 characters are accurately recognized by OCR.

Optical Character Recognition Use Cases

The digitized advancement of OCR is maintaining its importance in every field. These are some of the critical use cases and applications of OCR.

OCR in Banking

The Banking Sector has become the largest consumer of OCR technology by providing easy verification and enhancing security. This sector deals with many important documents like checkbooks, bank statements, passbooks, customer records, and more. The Loss of one single document can cause a loss of billions, which can be bad for business. OCR enhances the customer experience by ensuring security, improving data management, and optimizing risk management. It also helps prevent fraud by scanning signatures electronically and providing real-time verification for checks deposit. 

OCR  in Healthcare

OCR technology is becoming more prominent in the healthcare industry to keep all medical reports like CT scans, blood reports, MRIs, and X-rays digitized in one place. You can rely on OCR for accessing your medical information anywhere/anytime, by patients and doctors alike. OCR is capable of scanning, searching, and storing your health information. In addition, every hospital is using OCR to keep track of their patents, medications, and reports.

OCR in Traveling

OCR technology transforms the travel industry by providing passport scanning, data storing, flight and hotel booking applications. Most mobile travel apps and airports use OCR to ensure passengers' security and store their data. 

OCR for Historic Preservation

Countries like India have a rich history of mysteries. However, accessing and carrying 100 years old books with 1000 pages is not possible. Therefore, OCR made it possible by converting these historical documents into easily searchable PDFs, enabling easy access to historical documents like religious books, old newspapers, magazines, journals, and manuscripts.

OCR in Legal Documentation

Digitalizing, OCR enables safe and durable access to legal documents like property wills, loan papers, business transactions. Furthermore, the digitalization of legal documents becomes easy to share and access.  

Number Plate Recognition with OCR

Automatic number-plate recognition is a widely used application of OCR technology that enables the recognition of numbers or characters on license plates. Number-Plate recognition plays a vital role in searching for stolen cars, calculating parking fees (e-challan), and preventing road accidents.

Advantages Of Optical Character Recognition

  • Enhanced customer satisfaction: Improve customer satisfaction and experience by providing a digital and searchable format.
  • Improved productivity: Provide efficiency for employees. Keep their focus on what's more important, like, data accessibility issues and boosting business productivity. 
  • Improved accuracy:  Improve data handling, eliminate human errors, and ensure accurate results.
  • Speed up the processes: Increase access rates up to 10X by converting unstructured data into searchable information.
  • Cost-effective: Requires fewer resources for operation and reduces processing costs.

How can Indika help?

Our Computer Vision experts detect desired objects within images at the pixel level. We work on various AI use-cases like Financial AI and provide high-class image data. Our dedicated team is ready and capable of handling any project, regardless of size or complexity. Contact us today!

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