Optical Character Recognition is the process of reading and transforming written, printed, or scribbled characters into machine-encoded texts or anything else a computer can alter. It is a subset of image recognition and is typically used as a kind of data entry with printed documents or data records, such as financial records, sales receipts, passports, portfolios, and business cards, as input. The application is responsible for recognizing the characters and producing a written document from a digitized or scanned document.
In this article, we discuss definition, use-cases, benefits, limitations, and alternative of optical character recognition in different industries. At the end, we answer some of the most frequently asked questions about the OCR technology.
So, let's jump right into it:-
"OCR or Optical Character Recognition is the recognition of text from printed or handwritten documents and images in order to distinguish alphanumeric characters using technology."
Using technology that detects characters and letters and converts them into words and phrases, the OCR converts a picture into a searchable text. Humans have the capacity to glance at a page and nearly instantly recognize and comprehend the distinct letters, words, and phrases, but machines cannot do that. When a computer "sees" a picture, such as a page of printed text, the image consists of meaningless black and white pixels; the computer has no innate knowledge of the letters and words.
OCR software processes the characters in such a way that a computer can now read and recognize text: letters, symbols, words, etc. After OCR processing, a user can search scanned documents for certain keywords and phrases. When you combine document scanning with document recognition and text recognition, you transform your stack of paper records into digital files that are searchable.
In addition, a scanned document that has been OCR-processed can be utilized as an editable document, allowing you to modify the text as needed (in certain situations). This is the case when libraries digitize their historical collections and OCR the scanned documents so that volunteers may read and edit articles as needed. This approach is labor-intensive, as it consists primarily of human data entry, yet it is particularly effective for some applications.
Another instance would be if you needed to study some data, say from a report, but there are too many files to manually go through each one to get the data you require. You could scan the printed text and use OCR to produce searchable files; from there, it would be a matter of extracting research-relevant data. It's not perfect, but it's likely a lot more efficient than reading dozens or even hundreds of pages to discover a few pieces of information!
Let's put this definition into perspective, and look at an example.
What do you see in the below image?
Most likely, you would see the capitalized English character "A". Your mind has already done some preprocessing for you to identify light and dark regions, strokes and other features such as the triangle in the middle surrounded by darker regions.
However, this is what the computer sees when it sees the same image.
A computer simply 'sees' 1s and 0s. It has no cognition of what the patterns of ones and zeros represents to humans. OCR is the technology that converts the pattern of ones and zeros to machine readable data (eg. ASCII, HTML, JSON).
OCR technology helps computers understand printed and handwritten information by converting it to machine readable data.
Lets further expand on the example above.
Suppose you're an OCR computer program presented with lots of different letters written in lots of different fonts; how do you pick out all the letter As if they all look slightly different?
You could use a rule like this: If you see two angled lines that meet in a point at the top, in the center, and there's a horizontal line between them about halfway down, that's a letter A.
Apply that rule and you'll recognize most capital letter As, no matter what font they're written in. Instead of recognizing the complete pattern of an A, you're detecting the individual component features (angled lines, crossed lines, or whatever) from which the character is made.
Most modern OCR programs work by feature detection. However, rather than creating specific rules for each letter, they use neural networks for it. How neural networks work is much more complicated and out of scope of this article. In short, neural networks automatically detect features provided that it is trained on a large number of samples of the character it is trying to detect.
OCR software tries to recognize characters in the image /document by slicing the image into smaller pieces and then passing each piece through a neural network to check if it contains a character and to find closest matching character. Modern OCR programs such as Google Vision and Tesseract then combine these characters based on the spacing between them to give word representations.
OCR algorithms may be based on classic image processing and machine learning-based approaches or on deep learning-based methodologies.
There are three fundamental steps in the optical character recognition process:-
Typically, OCR software pre-processes pictures to increase the likelihood of successful recognition. The objective of picture preprocessing is to enhance the real image data. In this manner, undesired visual distortions are reduced and particular image characteristics are emphasized. These two procedures are essential for the subsequent phases.
For true character recognition, it is essential to comprehend "feature extraction." When input data is too vast to process, just a subset of characteristics is chosen. The selected traits are presumed to be the most significant, while those deemed superfluous are disregarded. The performance is enhanced by utilizing the smaller data set as opposed to the initial huge one.
This is essential for the OCR process since the algorithm must identify certain areas or forms of a digital image or video stream.
Post-processing is an additional mistake correcting approach that contributes to OCR's high accuracy. A lexicon can be used to restrict the output and increase the precision. So, for instance, the algorithm can fall back on a list of terms that are permitted to appear in the scanned page.
In addition to identifying appropriate words, OCR can also read numbers and codes. This is helpful for detecting long sequences of numbers and letters, such as serial numbers used in a variety of sectors.
Some providers have begun to build specialized OCR systems in order to better manage varying types of input OCR. These systems are able to cope with specialized pictures, and in order to increase recognition accuracy even further, they have merged a number of optimization strategies.
It is quite likely that you have used OCR technology in your life if you have used an app such as CamScanner to take photos of business cards. When you upload photos & PDF files to Google Drive, Google automatically scans them using OCR technology to identify text in them. Other applications of OCR are:
Here are some real-life industry-specific use-cases of Optical Character Recognition technology:-
Among the most common applications for OCR is the digitization of books and unstructured materials, which facilitates human-to-human communication. Google Translate's OCR technology, which allows users to read in any language, is one such example.
Bank statements need a substantial quantity of data input for banking activities. Checks may be deposited digitally and processed within a few days with OCR-based check depositing tools in mobile banking applications. Monitoring and evaluating your customers' data, including personal and security information, is another application of OCR in the banking sector.
Business OCR can also contribute to the rapidly expanding insurance industry. Specifically, OCR can automate the processing of insurance claims for speedier transactions.
OCR enables legal businesses to scan their printed documents, including affidavits, judgments, files, declarations, and wills, among others.
The OCR may contribute significantly to the healthcare business. OCR technology may digitize data from X-ray reports, patient histories, treatments or diagnoses, tests, and overall hospital records.
With OCR, travelers and hotel guests may now check in instantly by scanning their passports to a hotel's website or mobile app.
With mobile OCR, users may now redeem certificates by scanning their mobile phones for serial numbers.
Unquestionably, outsourcing data collecting is required to improve the management of actionable data. Even more advantageous is the introduction of corporate automation processes, notably machine learning, which can function 24 hours a day, seven days a week, and substantially quicker than human specialists, automatically improving data collection outsourcing procedures.
As machine learning evolves, it extends beyond data collecting and has major applications in several sectors.
Lending and Insurance enterprises use tons of data for underwriting and claim settlement. Similarly, logistics, healthcare, law, IT, and commercial real estate are heavily dependent on data. OCR makes it easier to extract data from all these documents and use it for analysis:-
Now is the time to look at best OCR software in the industry:-
Docsumo is an intelligent document processing software that focuses on data extraction and financial document processing. This comprehensive solution addresses the enterprise-level document processing automation requirements of a business.
Scan images and PDFs and convert them to editable text, tables, and digital files. ABBYY Flexicapture is excellent for large organizations. This optical character recognition software is precise, efficient, and manages batch processing in a manner that is unparalleled. It is ideal for decreasing manual data entry and input, giving you more time to focus on optimizing and expanding other parts of your organization.
Textract has the ability to extract information from scanned documents, printers, forms, and tables. It is perfect for scanning professional papers such as resumes, contracts, and books. Formatting is automatically identified and maintained, allowing you to prepare documents without having to manually modify the layout. Amazon Textract is one of the most effective OCR programs for medical records, financial reports, and other documents with abundant structured data.
Google is often the greatest at virtually everything, but their OCR technology is fairly restricted. According to several reports, the PDF tool is not precise enough for corporate use; hence, it has been ranked one of the top OCR programs for individuals. It is free to use, and you may employ rudimentary document editing and formatting tools. Additionally, you may convert your content to searchable PDFs. Important portions like tables, footnotes, and columns may not be detected by Google Doc AI.
Docparser is an OCR program best suited for financial documents based on a template. It is among the most sophisticated OCR tools on the market for processing financial documents.
There are 2 main shortcomings of OCR technology: Accuracy and Text categorization. Let's talk about both, and more in detail:-
One of the issues with OCR technology is that the accuracy may not be 100%. For example in the image below "21.08.2018" could be captured as "2I.O8.2OI8". Hence, you need a second system that validates the output of the OCR engine.
OCR technology identifies characters and then combines those characters into words. However, for business use, it is important to identify what those words mean. For example, OCR technology will give the output “Invoice No: 12345” where “Invoice No” represents the “invoice_number_key” and “12345” represents “invoice_number_value". This is where you need intelligence built on top of base OCR technology to make the identified text usable.
When individuals snap photographs of their ID papers with their cell phones or webcams, the images must typically be de-skewered and reoriented if they were not correctly aligned, so that the OCR system can extract the data.
OCR must frequently transform a color/grayscale picture to black and white to eliminate fuzzy text and better distinguish black and white text from its backdrop.
What happens if there is glare or the user moves slightly when their ID is being photographed? When there is glare or blurriness in the ID image, the likelihood of data extraction errors increases dramatically.
Data capture requires more than just OCR
Multiple processes are necessary to extract and arrange the information when consumers take a photo of their ID with their smartphone or webcam. The first step is to properly identify the type of identification present. This enables the engine to appropriately arrange the information read by the OCR, which includes determining the first name, last name, date of birth, and any other relevant field. Without additional AI or technology specifically trained to distinguish ID types, OCR alone will lack the precision required to combat fraud and provide a positive user experience. That's where Intelligent Document Processing comes into play:-
As we mentioned earlier, "Data structuring requires more than just OCR" can't be more true. OCR can recognize characters but it can't assign context to the text. In a way, OCR reads characters but it doesn't understand it. With the help of Artificial Intelligence and Machine Learning, Intelligent Document Processing(IDP) not only reads text but assigns context in order to provide more accuracy and usable data for analysis.
Let's take a look at where IDP fairs compared to OCR:-
Not only does IDPs rapid processing save time and money, but it also allows team members to focus on other business objectives. It's quite a revolution to be able to extract and categorize data in seconds as opposed to hours, which is why organizations constantly desire fast speed.
The future of technology favors products that adhere to simplicity and the "easy-to-use" phenomena, particularly in the technology sector. Even the most complicated systems seek easier solutions for their clients; this is not rocket science. In light of OCR and Acodis, OCR is considered to be less "easy" to set up and begin than the AI processes.
While a completely automated data processing system is not on the wish list of every organization, this feature is crucial to your and your company's productivity. This aspect is absent from OCR, necessitating that users regularly submit templates and monitor data processing. This is, in all honesty, a waste of time.
OCR is capable of interpreting simple text, numbers, and symbols, but it lacks Acodis' cutting-edge contextualized knowledge. Using OCR technologies to contextualize and alter important dates on an insurance policy, for example, would not be feasible.
Furthermore:
OCR can identify some pixels as the integers 1 9 8 0; however, it does not comprehend that this is a year and part of your Date of Birth.
IDP does, though. As humans, we rapidly comprehend the meaning of particular words, and IDP does the same for you. It can interpret text and documents.
When it comes to integrating data automation, the capacity to be efficient is a recurring topic. Unlike regular OCR, IDP is able to learn and evolve without the need for ongoing assistance.
Unlike OCR, IDP makes it possible to install the program into your organization with minimal to no complications or concerns. This function can save both time and money. Being readily integrated relieves you and your team of responsibility if you lack technological expertise.
Below we answer a list of common OCR FAQs for those who want to know more about this technology:-
OCR stands for Optical Character Recognition.
It is the technology used for scanning numbers, letters, shapes, and images from all sorts of documents. It is capable of reading special characters, symbols, and paragraphs from PDFs, spreadsheets, and various electronic files as well.
OCR’s history traces back to the 1920s when physicist Emanuel Goldberg created a machine that became capable of reading characters and converting them into telegraphic codes. The evolution of modern OCR took place after the 19th century when neural networks and the field of Natural Language Processing (NLP) made advancements in technological innovations.
The definition of OCR scanning is to recognize and read information from physical documents, scan it, and process the data into electronic formats. The meaning of ‘scanning,’ is to retrieve information from documents and process it in file management systems.
OCR programs store information as editable text or as documents on computers. For example, if you scan a piece of paper, OCR technology will enable you to extract data from this scanned image. This process involves converting the characters from these images into a machine-readable format.
OCR software programs let computers recognize text from physical documents, clean it up, and make it easier to interpret. OCR algorithm preprocess images from these documents and prepare them for reading in order for better chances of recognition. Common OCR scanning techniques include character isolation, aspect ratio scaling and normalization, de-skewing documents, and converting images to black and white photos for distinguishing text..
Zonal OCR is a subset of OCR technology which lets users scan specific “zones” or regions of documents and ignore the rest. This is useful for identifying the key-value pairs and line-items in a document, and save it instead of processing entire documents.
OCR technology is used by different industry verticals for the purpose of scanning, storing, processing, and sharing documents. Banks do data capture and extraction using OCR algorithms to archive client-related paperwork and make digital content more accessible. Signature recognition and validation using OCR is used for detecting fraud in documents and identifying cases of forgery for processing loan applications.
The logistics industry deals with huge volumes of data and requires authorities to identify inaccuracies in documentation. OCR solutions make it easy to automate process workflows, capture and validate information, and forward alerts as EDIs to stakeholders. The logistics industry is going paperless and OCR software lets employees save time and make remote work possible by removing the need for their physical presence when it comes to submitting relevant documentation.
Real estate industry uses OCR to get faster and accurate data analysis for verifying properties. Robotic Process Automation technology embedded with modern OCR solutions helps companies save the total cost of ownership and process more than 50 million documents a year, thus drastically increasing efficiency and generating savings in sales due to genuine paperwork. Commercial real estate deals automate the underwriting process and extract data from rent rolls for faster processing using modern OCR solutions.
OCR is used by insurance companies for filing claims, performing customer profile analysis, and automating data capture to save time and reduce errors associated with manual data entry. It can take over a 100 employees to process 10,000 documents a month but OCR in insurance can finish document processing in a matter of days!
Automated data extraction via OCR helps businesses in cutting costs and being more efficient in document processing. OCR offers users the following benefits over manual data entry:
1. 99% Data Accuracy – Manual data entry is laden with mistakes due to human-error and gets details often overlooked. Automated OCR solutions ensure up to 99% data accuracy, are precise, and do not misinterpret or miss details.
2. Easy Document Management – Intelligent OCR solutions read information well and make it easy to store. Document management becomes convenient for businesses as they digitize files and save in document processing system
3. Quicker Data Processing - OCR technology is 10x faster than manual data entry and greatly improves the speed of conversions from scanning to digitizing documents
4. Reduced Long-Term Costs – It is agreed that the initial costs of investing in intelligent OCR solutions is high. But the long-term costs of using these programs are low since they don’t require much maintenance.
5. Improved Customer Service - Customers often want quick and easily accessible ways of systematically storing and retrieving documents at blazing fast speeds. OCR makes customer onboarding seamless for businesses and drastically improves their experience online
OCR technology is generally accepted to be 98% to 99% accurate when it comes to reading and interpreting information correctly from documents. This means that for a 1,000-page document, up to 980 or 990 characters are accurately read by the software and recorded electronically.
Reliable OCR solutions like Docsumo don’t just have 99%+ page-level ocr accuracy but a high level of field-accuracy as well. High field-level accuracy scores let users achieve true automation when it comes to intelligent data entry and these programs require minimal manual review after data is entered by software algorithms.
OCR APIs are designed to transcribe text from handwritten documents for interpretation by machines. Popular use cases of OCR APIs include banking, finance, legal sectors, educational institutions, and the real estate industry. For legal documents, you can use OCR APIs to transcribe documents such as affidavits, judgments, filings, etc.
OCR APIs are used for automatically processing invoices, receipts, bill of laden, and extract information from tax records. Dedicated APIs are available so that users can scan KYC documents, identify survey templates, and categorise text from a wide array of documents across different sectors and industries.
The most popular OCR APIs in the industry are:
OCR is used for extracting text data from images and classifying it using intelligent analysis. However, even OCR has a set number of limitations which are as follows:
Optical Character Recognition (OCR) and Intelligent Character Recognition (ICR) each have their own use-cases when it comes to document processing and scanning. The key difference between the two is the way data is read from paper-based documents. OCR is best used for scanning text-based documents and converting them into digital files. There is no need to manually retype data when you use OCR software and it is considered to be a very cost-effective solution for businesses.
ICR, on the other hand, is ideal for reading handwritten fonts and different styles of cursive text. It can recognize and convert multiple styles of handwriting effectively and is powered by intelligent neural networks which are capable of automatically updating databases.
Although it is more expensive than OCR, it can save countless hours of time since it virtually reads any font and prevents human input errors associated with handwritten data entry.
If you’re new to the world of OCR and want to give a test drive, the best way to get started is by using the Docsumo free online OCR scanner. If you have a few document samples ready, you can upload your PDFs and image files to extract data automatically. You don’t need to install the software in your system. It’s completely free to use and there are no usage limitations.
Another free OCR tool we recommend to see the technology in action is the Docsumo OCR Chrome Extension. You can use it to scan text from websites, blogs, news articles, forums, and a variety of online portals. The data read can be translated into different languages like Spanish, Portuguese, German, etc. as well at no additional cost. Docsumo’s OCR Chrome Extension is also capable of reading text from visuals, graphical elements, video thumbnails, and a variety of images online.
Docsumo uses proprietary machine learning algorithms and AI technology for automating data capture in businesses and enterprises. Besides enjoying complete data privacy and legal compliance, you can use our intelligent OCR tools to automate document processing and improve productivity at work.
To get a first-hand experience of how intelligent OCR can benefit your business, sign up for a free demo with Docsumo and experience the difference today!
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