In a world governed by digital transformation, the timeliness in data transmission is now the basis of the smooth functioning of every company, but sometimes, extracting data from a variety of sources, including complex documents, can be a complicated and time-consuming operation. Data processing automation tools can sometimes have significant limitations when processing unstructured or semi-structured files. In this scenario, Intelligent Document Processing (IDP) is framed, which is a particular type of document processing in the form of intelligent automation that uses advanced technology to extract data from documents, e-mails, PDFs, images and other files and convert them into structured and usable data. This intelligent document processing process allows companies to eliminate manual data processing tasks, greatly improving processing times, reducing costs and eliminating any errors.
The IDP, in fact, is nothing more than a specific automation technology of a given workflow that scans, reads, extracts, categorizes and organizes meaningful information in formats accessible from large data streams by processing paper documents, PDFs, Word documents, spreadsheets etc. Its main function is to extract valuable information into large data sets without human input. The advantages of this automation process are several: through artificial intelligence, IDP can eliminate the need for manual data entry and processing by increasing the speed at which data can be processed while reducing costs, potential errors and increasing the efficiency of the activity. Currently, we often talk about three specific volumes of data: structured, unstructured and semi-structured. The former are organized and more easily read by human data controllers. Unstructured data, on the other hand, takes a long time to process and analyze, and finally semi-structured data falls in the middle, so intelligent document processing solutions can automate these sets.
What technologies support Intelligent Document Processing
The technological foundations on which the operations that characterize IDP are based include artificial intelligence and optical character recognition (OCR), which contains several subcategories of AI, such as computer vision (CV) and natural language processing (NLP). Intelligent document processing solutions work well with robotic process automation (RPA), such as optical character recognition (OCR), which converts typed, printed, and handwritten text into a machine-readable format. Although OCR solutions possess a certain “intelligence”, they only interpret what they “see” and therefore only decipher the meaning of the documents. Computer vision (CV), on the other hand, is a subset of artificial intelligence that deals with understanding and extracting meaning from digital images. Unlike OCR, which focuses on text recognition, the computer vision process can analyze the layout of a document to identify and extract data from non-text elements such as tables or graphs. We also see the aforementioned natural language processing (NLP), which is a subset of AI that extracts meaning from unstructured data, which allows companies to observe data in seconds. Finally, among the most common solutions are artificial intelligence, the science of creating, training and distributing software models that mimic human intellect. AI models can make their own judgments and predictions once trained on a wide range of data (as a result, models learn to “understand” imaging information and investigate the meaning of textual information as humans do). At the same time, we see Robotic process automation, which integrates Intelligent Document Processing (IDP) since it is not part of the technology stack, performing tasks such as processing transactions, manipulating data, and interacting with other enterprise IT systems.
What is the difference between IDP solution and OCR technology
There are various types of document processing, but as has been observed so far, IDP (intelligent document processing) is a technology that uses AI to extract information from documents, while OCR (optical character recognition) technology is capable of extracting text from images using a certain document processing software, rather fast but not precise enough, sometimes. IDP more accurately extracts information from documents that OCR does not support, and allows users to capture data from any type of document.