Businesses use a huge amount of paper every day, such as invoices, receipts, contracts, forms, and legal documents. Reading those documents by hand and entering the data into a computer takes a significant amount of time, costs businesses money, and is prone to errors, which is where OCR (Optical Character Recognition) comes into play.
OCR technology allows computers and other devices to read images of text. Even if the item is in a scanned PDF, a photograph taken on a mobile phone, or printed, OCR will allow that image to be processed as if a human were reading it at the same speed as an electronic device (the computer).
This guide provides an overview of OCR technology, how it functions, where it can be used, and what its real-world limitations are.
What Does OCR Mean and How Does It Work?
Optical character recognition (OCR) is a form of technology that allows you to capture text from an image and transform it into editable, searchable, and usable digital content.
When a document is scanned or taken with a camera, it will be saved as a picture file. The OCR software analyses the picture file, identifies the characters that are present in that picture file, including letters, numbers, symbols, and then converts those characters to a format that computer systems can comprehend. This allows for automated data extraction, indexing, searching, and integration with business software.
Today’s versions of OCR utilise pattern matching, language models and machine-learning technology.
OCR Process: Step-by-Step
Using the OCR Workflow Approach, you can convert a scanned image into a live and searchable version of that image through multiple steps:
The first step is to get the image into the computer. This can be accomplished by scanning it, downloading it as a PDF file, or taking a digital photograph of the original document with your smartphone.
The next step is to prepare the OCR image for text recognition. The OCR system will improve the quality of the photograph or scan by adjusting brightness levels, removing background noise, correcting for contrasting angles, and adding contrast to the image being processed. The success or failure of OCR results is primarily due to poor-quality images.
Once the OCR image has been prepared, the OCR software will perform the actual character and word recognition. OCR systems will identify characters, combine them to create words, and apply recognised language rules to correctly translate the text.
The final stage of the OCR text recognition process involves converting the recognised text into different file formats, such as plain text files, Search Engine Optimised (SEO) PDF files, Excel files, JSON files, or structured database fields for downstream application processing.
Types of OCR
The type of text will dictate the type of OCR available.
OCR for printed text is by far the most popular and accurate. It is typically used for documents that are produced on a computer, such as an invoice, contract, bank statement, or report.
While OCR can also be used for handwritten text, it has certain restrictions. It is possible to recognise basic handwriting; however, the degree of accuracy will vary based on how clearly and consistently the text has been written.
OCR that supports multiple languages enables a document to be processed in several different languages, using different alphabets or writing systems. Advanced OCR technology can automatically identify the language of a document, so that regional and international documents can be processed correctly; thus making it a requirement for global businesses.
Common OCR Use Cases
OCR is rapidly being adopted throughout various sectors to expedite document-centric processes.
In finance and accounting, OCR aids in the processing of invoices and receipts as well as the reconciling of expenses through the fast extraction of data from these documents.
Financial institutions also utilise OCR for their customer due diligence (CDD) processes by scanning and processing KYC documents (e.g., Aadhaar Card, Passport, PAN Card, and Address Proof).
The legal department of enterprises uses OCR to digitise their contract and agreement documents, making them searchable and easier to analyse.
Both human resources (HR) and operations utilise OCR for applications, forms, and onboarding documentation.
As shown by the above use cases, OCR minimises the manual workload associated with many document-based transactions and accelerates the completion time for these transactions.
Common OCR Challenges
Despite its many strengths, OCR (Optical Character Recognition) has limitations.
The primary challenge associated with OCR is the poor quality of scanned documents, consisting of unclear image resolution, shadowy images, and inconsistent lighting.
In addition to the difficulties of tables and complex layouts for basic OCR technology, many of these issues exist because the rows/columns are not arranged in a clearly defined way.
Cursive or inconsistent handwriting poses an even greater obstacle to manually entering or reading handwritten materials.
OCR technology has difficulty distinguishing between stamps, signatures, watermarks, and logo images.
OCR technology also suffers from skewing and rotating images, that make preprocessing difficult.
It is because of these limitations that many organisations cannot rely on OCR as a sole source of functionality to achieve mission-critical process automation.
Factors That Affect OCR Accuracy
OCR results’ accuracy is influenced by many things listed above.
Typically, when image resolution is increased, the accuracy of recognition improves.
Accuracy can be improved when a document is clear in its formatting (i.e., using consistent fonts) and alignment of text.
When there is less noise in a scanned document, when a clean background is used, and when a document is aligned correctly, OCR engines will produce a more accurate output.
It is easier to process documents that use predictable layouts compared to documents that have an unstructured format.
The selection of the correct OCR application based on your document type also plays a major role in obtaining accurate results.
OCR vs IDP: What’s the Difference?
OCR (Optical Character Recognition) is specifically designed to read text from images and convert it into a digital text format. It does not, however, understand the meaning or context of that information, nor does it validate whether what was read is correct.
On the other hand, Intelligent Document Processing (IDP) goes beyond OCR by adding to it the ability to understand documents, validate data, classify them and apply business rules to them.
Essentially, IDP is the combination of OCR, understanding and validating documents. In addition to extracting data from a document, IDP also checks that the information extracted from the document makes sense, is in the correct format and integrates with the existing workflows of a business reliably.
For example, a typical OCR system will extract an invoice total, while IDP will verify that the invoice total matches the line items on the invoice, as well as the vendor rules and the tax logic.
Best Practices for Using OCR Effectively
To maximise the effectiveness of OCR, it is important to begin with high-resolution scans and/or images.
The use of templates for structured documents (e.g., Invoices and Forms) can help to ensure the use of consistent formatting.
Develop and apply validation rules to identify errors as they occur, rather than relying simply on OCR results.
Incorporate post-processing workflows into the OCR process to assist with handling exceptions and edge cases.
For high-volume automation, look for automation solutions that offer capabilities beyond traditional OCR capabilities.
Final Thoughts
OCR serves as the fundamental building block for many Digital Transformation projects and all forms of Automated Data Extraction solutions, and it has been shown to save considerable time, reduce significant amounts of manual effort and allow the implementation of automated processes within many different industries. However, OCR will be maximally effective when used in combination with intelligence, validation, and business context.
Need more than just OCR? Learn more about Intelligent Document Processing (IDP) with the Snoh Fusion Platform to gain capabilities that go beyond just identifying text to provide reliable, automated processing of all document types on an end-to-end basis.
FAQs
What is OCR?
Optical Character Recognition (OCR) converts text from images, scanned documents, or PDF files into an editable and searchable digital format.
How does OCR work?
After scanning an image, OCR software uses pattern matching to identify characters and turns those characters into a format that can be processed by a computer.
What is OCR software used for?
OCR technology is widely used to extract text and Data from invoices, receipts, forms (including ID), contracts and many more document types.
Is OCR 100% accurate?
No, OCR indicates how accurately a person can use the software based on the quality of their scan, the complexity of the document’s structure, the resolution of the image and how well the text is printed or written.
Can OCR read handwritten text?
OCR can read simple handwriting, but it does not offer great results since it is only capable of producing good results based on the unique handwriting styles associated with the user.