The Evolution of Document Processing: From OCR to IDP
Picture a world where machines read handwriting better than humans, where data is extracted from documents in the blink of an eye, and where unstructured information becomes a wellspring of business insights. This isn't science fiction but the true story of Intelligent Document Processing (IDP).
Today, together with Sergey Jermakov, COO of Graip.AI, we will explore how once a dream of the smartest becomes everyday life and will trace the evolution of document processing.
Understanding the Origins: OCR Technology
Imagine you're at a conference, and someone hands you their business card. Instead of manually typing the contact information into your phone, you use Google Lens. You simply point your phone's camera at the business card, and voila! The text is magically converted into digital form. This not only saves you time but also reduces the chances of manual input errors. To make this happen Google Lens employs Optical Character Recognition technology.
OCR has a fascinating history that traces back to 1914 when Emanuel Goldberg introduced a groundbreaking invention. He devised a machine capable of reading characters and translating them into telegraph code. This innovation utilized movie projector technology to handle microfilm and employed a photoelectric cell for pattern recognition to identify the correct records.
Goldberg's dedication to enhancing OCR technology persisted over the years, leading to the development of what can be considered the world's inaugural search engine. This pioneering device utilized OCR to comb through microfilm archives, searching for specific patterns of characters. Remarkably, the U.S. patent for this "statistical machine" eventually found its way into the hands of IBM, signifying the enduring impact of Goldberg's pioneering work in the field of OCR.
OCR marked a groundbreaking advancement, enabling machines to recognize printed characters and transform them into machine-encoded text. This innovation laid the foundation for digitizing written content and automating fundamental data entry tasks.
Nowadays, OCR technology is versatile and widely employed in various applications, from banking and finance to education and healthcare. Its ability to convert printed text into machine-encoded characters has simplified numerous aspects of our lives, making it an integral part of the digital transformation we experience today.
However, OCR, despite its significance, grappled with certain limitations. While excelling with structured documents sporting consistent formats, OCR struggled when faced with handwritten text and unstructured data.
Robotic Process Automation: Automating Routine Tasks
RPA Market Size
Another pivotal moment in the journey of automation occurred in the early 2000s with the emergence of Robotic Process Automation. RPA brought forth the idea of software robots, often referred to as "bots," designed to mimic human interactions with computer systems. The primary goal of RPA was to automate tasks characterized by repetition and adherence to predefined rules, spanning across diverse business functions.
Much like their human counterparts, software robots are able to comprehend on-screen information, execute precise keystrokes, navigate through complex systems, recognize and extract data, and perform a diverse array of predefined tasks. The key distinction lies in their efficiency and consistency; software robots outperform humans in terms of speed and reliability, all while operating without the need for breaks or refreshment pauses.
RPA adoption is rapidly increasing across organizations of all sizes to generate greater returns on investment and boost productivity. The robotic process automation market size was valued at USD 10.01 billion in 2022. According to Fortune Business Insights, the global market for robotic process automation is projected to grow from USD 13.86 billion in 2023 to USD 50.50 billion in 2030.
Amid this exponential growth in the robotic process automation market, it's fascinating to see how RPA is creating tangible benefits in various industries. For example, in the healthcare industry , RPA is significantly enhancing patient care. RPA bots handle appointment scheduling, claims processing, and even patient data management. This not only reduces administrative burdens but also minimizes errors, leading to improved healthcare outcomes.
Intelligent Document Processing: The Paradigm Shift
Unlike OCR and traditional RPA, IDP broke free from the confines of structured data. It possessed the remarkable ability to comprehend and extract information from unstructured documents such as invoices, contracts, emails, and more. IDP systems went beyond mere automation; they learned from data, adapted to evolving document layouts, and continually improved their accuracy through machine learning. This adaptability and learning capability were game-changers, setting IDP apart from its predecessors.
The true transformation came in the late 2010s, when Artificial Intelligence and automation converged to give birth to Intelligent Document Processing. IDP harnessed advanced technologies like Machine Learning (ML) and Natural Language Processing (NLP) to intelligently process documents, regardless of their format or complexity.
The Power of IDP Today
According to a report by MarketsandMarkets, the IDP market value is projected to reach $5.2 billion by 2027, with a compound annual growth rate (CAGR) of 37.5%. As we witness the rapid growth in the adoption of IDP and the impressive market projections, it's evident that businesses are embracing innovative solutions to address their evolving needs. However, the success of these solutions doesn't solely depend on the adoption of IDP.
Emerging technologies, such as AI and ML, have gained significant traction among leading organizations in today's digital landscape. These organizations are actively seeking distinctive factors to gain a competitive edge by delivering top-tier customer experiences that include real-time updates. The focus now revolves around elevating customer satisfaction through enhanced business productivity and improved communication channels.
When it comes to data extraction, clients are increasingly demanding better outcomes. Intelligent Document Processing is emerging as a superior alternative to OCR and RPA . The challenges that arose from manual document handling fueled the demand for digital solutions, leading to the development of automated tools. The widespread adoption of IDP aligns with AI's vision of its ubiquitous utilization, as it addresses numerous pain points faced by working professionals in various industries.
Modern IDP solutions with AI offer a comprehensive range of functionalities, including automated data extraction, document classification, data validation, and even insights generation. Industries across the spectrum benefit from reduced manual labor, heightened accuracy, and the potential to unearth valuable insights from their documents.
Modern IDP solutions with AI offer a comprehensive range of functionalities, including automated data extraction, document classification, data validation, and even insights generation. Industries across the spectrum benefit from reduced manual labor, heightened accuracy, and the potential to unearth valuable insights from their documents.
In parallel, the time allocated to document processing has been steadily increasing, while digitization initiatives in these processes lag behind. Meanwhile, customers have come to expect faster and error-free results. Consequently, the demand for IDP solutions has witnessed a substantial uptick.