To compete in today’s digital age, it’s critical to process document data quickly and accurately. The capacity to quickly discover, retrieve, and comprehend document data is essential for today’s businesses. Regardless of size, sector, or emphasis, document processing is a business-critical use case that affects productivity.
Let’s talk about document processing evolution in this blog. Let’s look at digitalization and optical character recognition (OCR) and how businesses may use artificial intelligence (AI)-powered document recognition to improve document understanding and produce value.
Using OCR To Convert Offline Data Into Online Data
Traditional document processing methods are inconvenient. Many businesses still face issues like erroneous labelling and time lost due to manual data extraction from non-digitized document processing.
To address these issues, businesses are turning to digitization. According to a 2019 M-Files poll, 41% of respondents focus on replacing paper forms with electronic forms, while 70% plan to increase document processing to include more born-digital documents, up from 39% in 2018.
Document processing companies have embraced digitization to assist organizations in converting physical documents to digital formats. Optical character recognition is at the heart of these procedures. Text is recognized by OCR technology in both physical items and photographs. The text is then converted into digital files through OCR, such as PDFs.
OCR-based solutions are crucial for easing document processing issues. Traditional OCR technology, on the other hand, has its limits.
‘Intelligent OCR’ Is A Step Beyond Internet Data
Assume you take a picture of a paper or scan it into your preferred system. The quality of the image you scanned now determines how you classify and extract data. What does this mean for OCR-based document processing solutions?
The quality of the underlying document processed determines how effective optical character recognition systems are. When OCR software can’t distinguish between characters like ‘3’ and ‘8,’ or ‘O’ and ‘D,’ problems develop. When OCR technology is incapable of evaluating the intricacies of a document depending on its quality or original form, the very issues you seek to prevent by utilizing OCR software can become new headaches.
Document Recognition Driven By AI
As AI capabilities improve, companies have begun developing and training machine learning (ML) models for OCR. Intelligent OCR engines, or model-based OCR engines, provide considerable advances in digitizing documents and text at scale while minimizing errors.
Intelligent optical character recognition enables businesses to digitize documents and photos previously challenging to scan, such as handwritten letters, checkboxes, and cross-outs, using legacy OCR technologies.
We’re barely scratching the surface of what’s possible when OCR is combined with AI. Let’s look at some of the possibilities and outcomes you can achieve when you start using model-based document processing and digitalization solutions.
Using AI To Improve Data Extraction And Document Classification
The first of several stages to extracting value from a document is to convert it to a digital format. After a record has been digitized, OCR software must determine what type of document it is dealing with and what information is significant.
Traditional OCR technologies might make it difficult for businesses to scale their document classification operations. Traditional OCR systems use simple algorithms such as header identification to classify document types. This technique may limit a company’s ability to organize documents at a granular level.
Companies are frequently limited to document templates or established “recipes” for a digitized text used to designate relevant fields to extract and “rules” for finding that field in the document once documents are categorized using a typical optical character recognition solution. Templates are a great place to start, but they’re static. You can make rules based on recurrent patterns in the data, a document’s position, or a position relative to something else in the paper that’s easy to find, like a logo.
Companies invest in template management and new template generation as their document processing efforts increase to deal with document variants that were not important during the initial installation.
Using AI for document classification and data extraction alters this dynamic, making operations more efficient. Once your data is digital, you can use trained models to explore deeper into documents, classifying document kinds and extracting useful information in an organized manner.
Model-based optical character recognition solutions can recognize a document type and compare it to a recognized document type in your organization. They can also parse and understand unstructured document text blocks. Once the solution better understands the content, it can extract relevant data based on intent and meaning. It can also handle updates and variations in your papers.
You can define the fields you want—the document’s taxonomy—rather than building templates and then teach the ML model how to identify these fields. The model can then learn from human validations of processed documents and change itself based on the incoming documents.
These capabilities give your document processing system more flexibility and scalability. The outputs also expand the possibilities for what you can do with the data.
Using AI To Enable New Insights And Actions
OCR using artificial intelligence (AI) for document classification and data extraction is a considerable step toward providing your company with automated and accurate document processing capabilities. When you consider the long term, you can start to layout a strategy for utilizing AI capabilities and doing more with the text you extract.
You can use AI to check for problems by referring to data from numerous documents or other backend systems. Let’s say an invoice amount is off, but it wasn’t due to an error in the optical character recognition process. You can use a combination of robots to extract data from various document kinds and systems to discover the source of the problem. This allows you to double-check data and find exceptions and issues unrelated to the OCR process.
You can also start using AI skills on data sets over time and historical context to develop forecasts and detect probable fraud anomalies. Let’s look at an example of insurance claim processing. The first step is to digitize a claim that has been received. The claim’s important information (such as the claim date, kind, and amount) is extracted. Then, using factors like recurrences and suspicious amounts, you can look at these data points and apply an ML model to detect individual claims that may be fraudulent.
AI enables these types of activities to be completed, considering the following steps on the path to document processing ecstasy. Document processing does not have to be a challenging experience. We’re passionate about assisting clients in OCR using artificial intelligence to streamline procedures and make life easier. Starting with optical character recognition and expanding it with AI can help you make document processing more valuable—and less time-consuming.
Do you want to learn more about how we can use AI to simplify and improve your company’s document processing operations and improve document understanding? Contact ONPASSIVE to know more.