The artificial intelligence (AI) revolution has unleashed a wave of innovation that is rippling across every industry. Among the most dramatic advances is the infusion of AI and deep learning into optical character recognition (OCR), which offers businesses significant new benefits via new modes of intelligent document management.
As Information Age notes, “AI OCR tools are sleeping giants in the wider topic of digital transformation.”
OCR has been an important element of document image processing systems since the early 1990s. The business value of OCR is derived from its ability to convert paper documents or images into searchable and readable documents, such as Word and PDF files, that can be stored in computer systems and accessed companywide.
Once converted into readable and searchable digital documents, electronic files can be stored in computer systems rather than folders, file cabinets, and warehouses. The digitized documents also can be easily modified rather than having to be retyped, which can add great value through flexible document management. All these factors have enabled businesses to increase efficiency and reduce cost using OCR-based imaging and document management systems.
However, there are a number of impediments that have limited the capability of OCR and added labor and cost, including difficulty in deciphering handwriting, in recognizing certain typefaces and characters, and in discerning poor resolution images, seals, stamps, tables, and other document elements that frustrate traditional OCR systems.
Among the most significant OCR weaknesses is its dependence on document templates. Invoices, for example, can present an endless variety of layouts. A template must be created for each individual invoice for OCR to be able to know where to locate and read specific types of information within the fields of the document.
For each unique form or invoice, templates must be designed and the OCR system must be trained to recognize the fields within the documents. In instances in which OCR cannot recognize data, workers must review the documents and correct the information manually. These drawbacks reduce the efficiency of OCR and add cost.
The AI revolution has been a game changer, infusing intelligence into systems across all industries. Besides advances in data science, the key factors that have contributed to the AI revolution include more powerful CPUs, massive amounts of inexpensive storage in the cloud, and in-memory computing.
The application of AI, machine learning, and deep learning to OCR is called intelligent data processing (IDP) and intelligent data capture (IDC), among other names. Machine Learning and Deep Learning are subsets of AI that are capable of self-learning, which can continually improve OCR’s recognition and analytical capabilities.
IDP improves OCR’s recognition capabilities by applying the most advanced AI technologies, including advanced algorithms, feature and pattern recognition techniques, natural language processing, neural networks, and machine vision.
A major advantage of IDP over traditional OCR systems is the elimination of the need for document templates. Through the ability to identify any field and data type in any document layout, IDP systems can locate and identify company names, dates, dollar amounts, order details, etc., regardless of where they appear in a document.
IDP systems can extract and analyze data from more complex documents than can traditional OCR systems. Besides improving the process of converting printed documents into readable and searchable digital formats, IDP can improve the indexing and categorizing of documents, and can add even greater value through the ability to understand the context of data within documents.
IDP can reduce the time, labor, and cost of document management, and can increase revenues and profits by applying business intelligence to the data extracted from documents and shared with other systems.
IDP offers benefits to organizations of all sizes and across all industries. IDP can improve the processing of forms, invoices, receipts, and other documents common to every business and agency, as well as industry-specific documents. These include core business processes such as document storage and retrieval, mailroom automation, HR automation, and data entry.
In applying IDP and business intelligence to mailroom automation, for example, incoming documents can be automatically analyzed, sorted, and routed to the appropriate business units. Data relevant to decision making can be extracted and incorporated in business analytics systems. Workflows can be created in which routed data is merged and analyzed.
Applying IDP to documents as they are converted also can improve security, fraud detection, compliance, and disaster recovery.
Document-intensive fields like banking, finance, insurance, government, and healthcare have been reaping the efficiency boosting and cost-saving benefits of OCR systems for years. With the rise of IDP, these industries are able to reap a new round of productivity and analytical benefits.
Banks, for example, are using IDP to process a wide range of forms and documents, from personal and corporate account documents to mortgage documents and credit application applications. Personal and corporate mortgage applications alone can consist of a host of document types, including tax returns, salary slips, desired purchase documentation, bank statements, photo ID, inheritance verification, income statements, balance sheets, and cash flow statements.
These documents contain data that must be digitized, searched, extracted, and verified. Manual processing can require weeks of labor and can introduce errors. Moreover, documents must be shared among different workers who need to access different information, sometimes simultaneously.
IDP enables the entire mortgage application process to be seamlessly automated. The IDP system can extract and verify data from all the supporting documents and update the core systems and databases. The IDP system can initiate any further processing that might be required and can flag exceptions that require human workers to resolve.
Similar IDP advantages can be seen in the insurance industry, where data from thousands of claims can be extracted, merged, and analyzed for trends and patterns that can provide valuable business insights.
In the healthcare field, IDP systems are improving the processing of medical reports, laboratory tests, invoices, insurance agreements, and other documents. Once the data is extracted, it can be routed and shared with other parties and systems. Patient symptoms and laboratory test results, for example, can be extracted and fed into specialized AI systems to aid in patient diagnoses.
Similar IDP advantages can be seen in industries across the board, including energy, travel, hospitality, transportation, manufacturing, government, and retail.
The power of AI is increasing exponentially as IBM, Microsoft, Google, Amazon, Salesforce, and many other major players pour money and resources into AI research and development.
By deploying AI-based OCR today, your organization will be positioned to reap an increasing wealth of benefits for years to come.
Roth Automation’s expert consultants and engineers have years of experience designing, deploying, and optimizing business automation processes. Roth Automation is partnering with best-of-breed providers to create state-of-the-art AI-based OCR solutions. Our combined expertise can assist you in accruing the maximum efficiency and cost-reduction benefits that can be gained by deploying AI-based OCR.