Common File Types You Can Automate Using AI OCR

Though optical character recognition (OCR) technology has been around for decades, recent advancements in computing and AI technologies like machine learning (ML), natural language processing (NLP), computer vision and neural networks have given us a more versatile version, without the limitations of traditional OCR – AI OCR document automation.

Though the primary function of OCR systems remains the same – to extract data from images, PDFs, and scanned or handwritten documents, and convert them to machine-readable and editable file formats, modern-day OCRs, as part of intelligent document processing (IDP) systems, also give you the edge of analytics and actionable insights for your business.

As Peter Sondergaard, VP of Research at Gartner Inc., says, “Information is the oil of the 21st century, and analytics is the combustion engine.”

Hence the relevance of AI-enabled OCR in the ever-evolving business environments cannot be understated.

Benefits of AI OCR in Document Processing Automation

Besides significantly reducing human effort in data entry and processing, OCR, coupled with AI tools like ML and NLP breathes new life into document processing. The benefits of an AI-powered OCR are:

• Improved Accuracy: An OCR system, traditional or modern, significantly reduces the chances of typos or other manual data entry-related factual errors as it captures data directly from the original source.
• Cost Saving and Improved Efficiency: OCR also reduces the costs associated with manual labor and physical storage of materials such as paper and other stationery. Also, along with improved accuracy, it improves efficiency by reducing the time taken for data processing.
• Enhanced Data Security: Digitized documents are less prone to damages or losses unlike physical documents, and hence are more secure. It also enables easy but controlled access to documents and implements restrictions.
• Improved Compliance: AI-powered OCR solutions provide advanced security and compliance features such as encryption, access controls, and auditing capabilities, to guarantee protection for sensitive information and ensure regulatory compliance.
• Scalability: AI-enabled OCR systems have the added advantage of being scalable with ease depending on the changing business needs as these can be integrated with legacy systems without much fuss.

Best File Types for AI OCR Automation

Almost any type of static images structured, semi-structured or unstructured, can be converted to usable data banks with the AI-powered OCR extraction process. Let’s take a look at some of the best use cases:

• Invoices and Receipts: Almost all invoices and receipts, unless handwritten, follows a standard format, and hence are ideal and the easiest for data extraction. They are mostly in structured or semi-structured formats. These documents usually have elements like vendor name, date, line items, invoice/receipt number etc., which an OCR system can easily identify and extract through keyword and template matching. Businesses like healthcare, retail, and manufacturing are most likely to need invoice processing and an AI-enabled OCR is best suited for the purpose.
• Forms: Different types of forms are used for different purposes across industries— claim forms for insurance, tax forms, application forms for loans or account opening in banks, admission forms in hospitals and educational institutions, service request forms and many more. These forms usually come as strictly structured or semi-structured files. Smart OCR systems can be trained to extract accurate data from these pre-filled forms.
• Legal Documents: Legal documents such as contract agreements, memorandum of understanding (MOUs), case files, historical documents etc. come in various formats and types, and can be classified as mostly unstructured data. Though AI-powered OCR for legal documents and contracts has not evolved enough to fully decode the nuances of these documents, it can still extract key data like names, dates, and reference numbers, which can be used for indexing and easy-search purposes.
• Resumes/CVs: Just like invoices and receipts, these semi-structured files usually have the same sets of key data like name, work history, contact details etc. AI OCR document automation can easily extract these data and store it in digital files for easy reference.
• Business Cards: Though not a big file with extensive data, these semi-structured files provide essential details like company logo, name, card holder’s title, contact details, etc. which are invaluable for businesses. Modern OCR systems with omni-font and optical mark recognition capabilities can easily extract these data from business cards and populate them in CRM systems for easy digital document management and sharing of contact information.
• Handwritten Notes: These mostly come as unstructured documents which are still not very easy even for AI-powered OCR systems to process. However, advancements in NLP and machine learning in OCR are improving the accuracy of these systems in processing handwritten documents. OCR systems with AI capabilities can extract key data points from handwritten notes provided they are legible, with good image quality and consistent handwriting.

Conclusion

OCR is inevitable for all kinds of businesses, and AI-enabled OCR is here to stay as the first stage of smart data analytics systems. It not only reduces the time wasted on manual data entry, but also helps enterprises to cut down on costs, improve efficiency and productivity, resulting in better ROIs.

DeepKnit AI has a proven track record of implementing different AI solutions for businesses across industries, and have the capability to provide custom made OCR tool that best fits your organization’s requirements.

If you’re planning to implement automated data entry for your business, feel free to contact us for a demo and consultation.