OCR is magical. Can you imagine a computer turning an image with text into a searchable, editable document? Also, given that the only thing that matters to the computer is pixels, not letters or words? This is made possible by OCR, or optical character recognition. It is a technology that lends meaning to text. The revenue forecast for the global OCR market is USD 26.31 billion by 2028, expected to grow at a CAGR of 16.7% from 2021 to 2028. OCR is increasingly used in several businesses such as FinTech, crypto, gaming etc. particularly for the purpose of onboarding new users. By using highly accurate OCR for onboarding, customers have a seamless experience. Are there any industry-specific benefits? Yes, there are. In FinTech, accurate OCR can reduce turnaround times, improving customer retention and detecting fraud. In crypto, it can help identify users who are flouting Anti-AML laws or indulging in illegal trading. 

What is an OCR Engine

Before we define what an OCR engine is, we need to understand what an OCR software is and does, as a whole. OCR software is a technology that allows data extraction from printed or written text from a document that has been scanned or an image. The data that is extracted from these documents or images can then be turned into machine readable format and can be used for data processing, searching, editing and a whole lot more. 

It is the OCR Engine that performs the actual character recognition, examining the pixels in an image and determining which characters they represent. It does the heavy lifting and gets the raw character data from the documents/images. Further, it analyzes the pixels in the image and determines what text character it can represent. 

There are four phases in an OCR engine: 

  1. Pre-Processing
  2. Segmenting
  3. Character Recognition
  4. Post-Processing

The accuracy of an OCR engine is affected by several factors, some of which are paper quality, quality of printing or ink used, smudges and other artifacts, and the language that is used. When you opt for an OCR engine, its ability to use AI/ML can be a gamechanger. AI/ML has increased the accuracy of OCR engines appreciably, with errors happening sparingly when the model is well-trained.

Now is it possible to have an OCR engine as an on-demand solution? Can it be used in just those specific cases where the existing algorithm does not give a satisfactory result? Short answer: Yes, this is possible if you use the HyperVerge OCR engine. HyperVerge allows your organization to connect to the Hyper Turing OCR engine using an API request. Such direct access to the Huper Turing engine allows the organization to increase the accuracy of OCR. In such cases, the response time to complete a request is of the magnitude of a few seconds. 

Today HyperVerge supports OCR in all of the ASEAN countries (including India), Nigeria, Brazil, and the US. This has allowed HyperVerge to familiarize itself with a wide variety of languages. It currently supports the Latin family of languages, English and ideographic scripts (like Mandarin and Japanese). HyperVerge does not support the Indian languages as of now. In every region that it is present in, HyperVerge offers its OCR engine on demand.

Many different kinds of documents can be supported by the Hyper Turing OCR engine. This includes both structured and unstructured documents. Examples of such documents are driver’s license, government identity and proof of residence such as Aadhaar, the voter ID 

HyperVerge has supported a lot of clients with its OCR engine on demand. When supporting a client, a few points are taken into consideration. These are use cases for the organization, the allocated budget, and how the whole OCR solution integrates into an existing system. Because of a direct connection via an API to the Hyper Turing engine, a large amount of training data, in this case samples of government IDs etc., is not required anymore when HyperVerge moves to a new geography. An intelligent model can be built with minimal samples even if the client doesn’t want to share any for security or privacy reasons. 

HyperVerge has worked with clients such as Byju’s, Razorpay and DTDC to set up an OCR system for easy user onboarding and tracking. Requests were completed in a matter of 2 to 3 weeks for Razorpay, and HyperVerge enabled Byju’s to optimize their sales pipeline in a matter of 1.5 to 2 weeks. The on-demand OCR engine also helped DTDC identify addresses in unstructured tracking labels on their parcels, mostly handwritten. This helped DTDC achieve better accuracy in their last mile operations. How did this happen? The on-demand OCR engine helped identify the ‘from’ and ‘to’ addresses, along with the mobile number, and destination and source pincodes on every parcel. The accuracy was lower as it would also depend on the quality of the handwriting. Yet an appreciable level of accuracy was still maintained. This greatly helped DTDC streamline their operations.

HyperVerge has been providing face recognition and OCR solutions to B2B enterprises since 2017. Five years down, HyperVerge is today one of the top five pioneers in the world when it comes to machine-learning and AI-based OCR and facial recognition.

With an AI/ML-backed OCR solution as robust as HyperVerge, it is possible to adapt easily to a new geography. Accurate identification for characters in over 50 languages in nearly half a million fonts present worldwide is what sets HyperVerge apart. Thanks to the Hyper Turing OCR Engine it does not fail to extract details from a document even over a poor connection to the Internet or from low-resolution or badly contrasting images. 

HyperVerge is a champion when it comes to OCR and an on-demand OCR engine. And it is set to become even better in the future, thanks to the continuous benchmarking against competitors on the AI models and the OCR code and the constant endeavors of the in-house team to leave no region anywhere in our world unsupported.