With the growing interest in OCR and Machine Learning, more and more business owners are looking for ways to apply this killing combination to optimize their business processes, and if you are one of them, this article is for you.
Let’s find out more about what OCR is, how OCR powered with machine learning is different from the original technology, and how it can be used in business.
What is OCR?
Optical character recognition (OCR), also known as text recognition technology, converts any kind of image containing written text into machine-readable text data. A typical optical character recognition system consists of three stages: image pre-processing, character recognition, and post-processing.
- Image pre-processing helps to remove image noise and increase the contrast between the background and text, which will help improve text recognition.
- At the stage of character recognition, characters are assembled into words and sentences, and then they are identified using pattern recognition or feature detection algorithms.
- Post-processing includes filtering out noisy outputs and false positives, combining recognized entities with their extracted meaning, checking for possible mistakes, etc.
OCR allows you to quickly and automatically digitize a document without manual data entry. That’s why OCR is commonly used for business flow optimization and automation. The output of OCR is further used for electronic document editing and compact data storage and also forms the basis for cognitive computing, machine translation, and text-to-speech technologies.
Advances in machine learning (ML) have given a new impetus to the development of OCR, significantly increasing the number of its applications. With enough training data, the OCR machine learning algorithm now can be applied to any real-world scenario that requires identification and text transformation.
OCR Business Cases
Modern OCR systems are used in security, banking, insurance, medicine, communications, retail companies, and other industries.
Use cases for OCR technology include checking test answers, real-time translations, recognizing street signs (Google Street View), searching through photos (Dropbox), and more. Optical character recognition is also widely used by security teams. This technology helps to analyze and process documents such as a driver’s license or ID for verifying a person’s identity. For each case, a completely different OCR solution is used.
OCR in Financial Services
Financial transactions involve a huge amount of data entry. Manual processing of this data takes a lot of time and effort while digitization of financial documents and extracting the necessary information from them using OCR makes business processes smooth and optimized. As a result, OCR technology improves customer onboarding and enhances the overall customer experience.
Optical character recognition uses in the banking and financial sector include the following:
- Client onboarding. OCR technology provides a fully automated onboarding process consisting of scanning an identity document (e.g. ID, passport, or driver’s license), extracting the necessary data using OCR (e.g. name, dates of birth, gender, photo, signature, etc.), and checking it. For example, the OCR engine can inspect in real time whether the provided signature matches the signature on the identity document.
- Scan-to-pay feature. The scan-to-pay feature uses optical character recognition to instantly capture invoice data and automatically process it. OCR can also act as an extra security feature when making payments. Usually, users store cardholder data in the application desiring not to enter the card number and other details every time. With OCR, all you need is to enable the OCR feature which extracts data in seconds for each new payment and then removes it.
- Receipt recognition. OCR allows automating data extraction from receipts for further accounting, archiving, or document analytics. You can find this feature implemented in financial assistant apps with money-tracking elements for automated data entry of expenses and expense categories. Expensify is an example of such an application.
- Loan processing. Automation of data entry makes the process of reviewing applications and approving or rejecting them much faster and more cost-effective for the company. AI algorithms can parse the required data from the application to determine if it should be approved or rejected based on the financial institution’s rules.
Use cases of OCR in finance are not limited to the above. The technology can be used for processing other financial documents like invoices, contracts, bills, financial reports, etc.
OCR in Healthcare
OСR cases in the healthcare industry are closely related to data management. The digitalization of medical documents and the efficient extraction of data from them is a critical aspect of the functioning of a healthcare institution.
By applying optical character recognition technology hospitals can translate papers into a digital format much faster and store them as PDF documents that can be easily searched using keywords. Electronic medical records solve one of the main problems of hospitals, the loss of medical information about patients. Also, OCR allows data to be pulled from certificates or test results and sent to hospital information management systems (HIMS) for integration into patient records thus forming a complete medical history of patients.
Pharmaceutical systems can take advantage of OCR as well. Powered with an OCR module such systems allow you to scan medical prescriptions and import them into software to check the presence of the medicine in pharmacy databases or even use it to control picking robots.
OCR technology is also used to help people with visual impairments. By scanning the text on the image, the OCR system provides the base for using text-to-speech technology. All you have to do is scan the text to get synthetic speech output. For example, the Voice Speech Scanner app uses the smartphone’s camera to capture a photo with text and then reads all of the text back.
OCR in Retail
Using OCR with machine learning, retailers can experience the rapid development of internal business processes and improve the customer experience by making the most of the existing data. For example, merchants can extract valuable insights from purchase order analytics to create more effective marketing campaigns, promotions, and manage pricing better. By converting invoices and receipts into digital format and incorporating them into accounting systems, retail companies get a chance to automate their accounting processes.
Implementing OCR is a great way to handle the large workloads of retail workers. With automatic data entry and data extraction, employees are left with only manual verification to achieve optimal results.
Cases of using OCR in retail are not limited to the above. The text recognition feature can address some specific challenges of retail companies. For example, the technology can be helpful for wine merchants who offer a wide range of products. With OCR-based wine label recognition, users can take a photo of a wine label and get product information such as reviews, descriptions, etc. to help them make the right choice.
OCR in Security and Law Enforcement
Almost any industry can take advantage of OCR as part of its security strategy. Using OCR powered by machine learning, companies have a chance to build advanced user authentication and verification systems. Usually, manual comparison documents with provided personal info and a selfie are used to verify the authenticity of the identifier presented by the user. The OCR model eliminates these manual efforts by scanning ID cards, passports, or driver’s licenses and checking their authenticity, comparing them with the info in the database.
In this case, the OCR engine must first recognize the document type. For example, if a user chooses to authenticate with a driver’s license, the document they upload to the system must conform to that document format. Then the system should analyze and process uploaded user documents to get relevant data.
Since documents of the same type may have a different format depending on the country or state, the system must be able to find and extract the necessary data from all variations. Using deep learning algorithms helps the OCR system understand the relative positional relationship among different text blocks and combine pairs of semantically connected blocks of text to find relevant data such as name, date of birth, etc.
It is also worth mentioning that secure authentication OCR software should have features to prevent spoofing attempts when parsing documents. Anti-spoofing techniques will help the system detect fake ID scans and other fraudulent attempts.
Limitations of OCR Technology and How to Overcome Them
Although optical character recognition is a widely used technology, it has some limitations, especially if we talk about classical text recognition systems. Combining OCR with computer vision and deep learning improves the accuracy of OCR in many cases, but it is important to understand that it is impossible to achieve 100 percent results and you will need additional software solutions to improve the outcomes.
The list of key limitations of optical character recognition technology includes the following:
The Lower the Quality of An Image, The Lower the Quality of the OCR Output
Common OCR errors include misreading letters, missing unreadable letters, or mixing text from adjacent columns. The most commonly used methods for normalizing an image include aligning and rotating the document, removing blur and applying filters, and deleting elements that are not characters (like tables, separator lines, etc.).
Complex Image Background
Elements such as small dots or sharp edges that make up the background can often be read as characters and distort the results of the text recognition process. To overcome the issue of noise presence such as dots, lines, stains, etc. in the background, nowadays OCR approaches use computer vision-based algorithms trained on augmented data sets.
OCR Works Better with Printed Text than With Handwritten Text
Handwritten fonts have hundreds of variations, which complicates the text recognition process. For handwriting recognition, the development team needs to train the OCR model using deep learning algorithms and advanced computer vision engines.
It’s worth noting that the quality of the dataset that is used to train the model affects the accuracy and speediness of results. In this case, it’s better to use less data, but the most relevant.
Optical Character Recognition (OCR) based on AI and machine learning is a widely used technology for text recognition and digitalization of documents. Even though OCR is not yet 100 percent accurate, its use cases are growing with the development of deep learning and computer vision. Today, one or another type of OCR is used in retail, communications, finance, healthcare, security, tourism, and other industries.
The definition of business goals greatly influences the approaches, architecture, and tools that will be used to develop OCR software. The data should correspond to the objectives of your project and be as real as possible.