Steven de SimseoDecember 16, 2023

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Steven de SimseoDecember 16, 2023

Intelligent Document Processing (IDP) represents the new frontier of office automation, offering a revolutionary solution for document management and processing.

Through the use of advanced technologies such as machine learning, optical character recognition (OCR) and natural language processing (NLP), IDP allows businesses to transform large volumes of unstructured or semi-structured documents. structured into accessible and manageable data.

This process speeds up routine operations, reducing workload and errors, and also paves the way for richer analytical insights and more informed business decisions.

In a world where efficiency and data intelligence are vital, intelligent document processing is emerging as a critical resource for businesses aiming for operational excellence and sustainable competitive advantage.

IDP and AI process huge amounts of information

Artificial intelligence allows you to process huge amounts of data and manage all aspects of it in a short time. This is what we call Big Data management: these databases being made up not only of a particularly large quantity of data, but also of data containing very heterogeneous and unstructured information, it is necessary, to receive understandable and useful information. To this end, implement systems based on artificial intelligence, a technology called Intelligent Document Processing (IDP) which makes it possible to organize data, label it and assign to each a specific description which allows it to be analyzed. identify and use them according to the use to be made of them.

When implemented within a business, Intelligent Document Processing technology also allows organizations – through the ability to structure data quickly and accurately – to increase productivity, retrieve documents in fewer time, be able to guarantee greater precision, automate document classification, and much more.

What is intelligent document processing

With the term Intelligent Document Processing , also called Intelligent Data Processing, we identify technologies based on artificial intelligence that enable the extraction and processing of large amounts of heterogeneous data, including unstructured data. In summary, it is a system that allows you to acquire the data contained in different types of documentation, in an automated way, reducing human intervention to a minimum.

The operating methods of IDP technologies make it possible, at the same time, to obtain very precise results in restricted terms: when an IDP system, in fact, analyzes a document, transforms the information – initially unstructured, or semi-structured – into usable data, which can therefore be grouped and processed, also with the help of other artificial intelligence systems such as natural language processing (NLP) , Computer Vision, deep learning and machine learning (ML).

In the business domain, IDP, through the transformation of unstructured data into structured data, helps provide document-centric processes are automated end-to-end, significantly accelerating operations. Indeed, in the absence of these systems, it would be necessary to have, within the company's organizational chart, figures specifically dedicated to the reading of documents and the extraction of data, with percentages of higher errors and in much longer times.

One of the advantages of IDP technologies is that they are also highly scalable systems (when combined, as mentioned, with other artificial intelligence solutions that work on the data structured by the IDP) and not invasive:

• When combined, for example, with an OCR (optical character recognition ) system (you hate ICR (Intelligent Character Recognition ), allows the machine to read a document, even unprocessed, in native digital format, or a non-textual element, such as a photo or a graphic, in order to then be able to classify, categorize, extract and validate it correctly;
• However, when combined with RPA or Robotic Process Automation systems, it allows repetitive tasks to be performed much faster, such as inserting rows of data from a (structured) database into a spreadsheet .

Intelligent document processing therefore allows:

• cost savings associated with processing large volumes of data;
• setting faster analysis processes with a high degree of automation;
• an increase in the precision of the processing carried out on the data;
• there reduction in the time required for so-called knowledge workers, i.e. those who carry out data analysis and processing activities, to process these documents, which otherwise would always have to be entered and cataloged manually;
• Automation of end-to-end operational processes ;
• there reduction of document retrieval times .

IDP converts unstructured data

As anticipated in the introduction, the goal of the IDP is to organize initially unstructured data. Indeed, when you acquire a document, the information it contains is not structured : it follows that even the information assets of most organizations suffer, in the absence of systems of this nature, from a deficiency organizational.

The organization of information, vice versa, and the selection among them of information relevant to the purposes for which these documents are acquired, allows companies to be more competitive and fully exploit the value of the information collected, as well than understanding and using this latest information to improve your processes, your customer experience, your business model, or to study data more easily.

Especially today, in an increasingly digital and automated world, the ability to extract data from documents in a short time becomes increasingly important to remain competitive. IDP technology, thanks to artificial intelligence, makes relevant data immediately accessible for the processing necessary for the company, thus simplifying the flow of information for simpler management and better business decisions.

Difference between OCR and intelligent document processing

In order to better understand what is meant by intelligent document processing (IDP), it is useful to also analyze the difference between this system and what is called OCR (Optical Character Recognition).

OCR software are programs that allow a machine to recognize characters, and therefore to read documents which – as expected – have not been acquired in native digital format, and are therefore handwritten, or from a scan. OCR systems also allow you to read information contained, as mentioned, in photos, graphics or other elements of various nature.

There can be different types of OCR, depending on the type of element they can capture:

• Optical character recognition (OCR) . OCR systems recognize handwritten or typed characters based on an existing internal database.
• OWR Word Recognition (OWR). This method is used for typed text, one specific word at a time, and is used for languages ​​that divide words with spaces.
• Optical brand recognition (OMR). The OMR type analyzes watermarks, logos, symbols, signs and patterns on a paper document.
• Intelligent Character Recognition (ICR). ICR uses data acquisition tools to read handwritten text or cursive text. This method uses machine learning and AI technology to analyze different elements of text (curves, loops, lines, etc.). ICR identifies and processes a single character at a time.

These OCR systems are usually integrated into intelligent document processing systems and constitute a fragment of them, as they help artificial intelligence to acquire information even from documents that do not conform to the classic electronic standard.

Cooperation between the two systems therefore allows:

• on the one hand, from Capture text from images, scans or non-editable PDFs : OCR, in fact, scans the document, corrects errors and identifies characters thanks to two main algorithms, pattern matching and feature extraction, then converts the data into electronic documents;
• on the other, to extract and detect information from natural language documents and to structure the information acquired by OCR systems in an organized and comprehensive manner.

The collaboration between the two systems therefore allows greater efficiency in the document acquisition and processing processes, allowing the classification of even images (in the thousands). It will be possible, for example, to analyze an identity document, extrapolate its personal data and automatically insert the same data into a form: activities which, carried out manually, would require days of work (with no guarantee of accuracy of information). data themselves).

Activities therefore of a typically repetitive and mechanical nature which, in this way, can be automated and accelerated, without requiring the performance of activities of an interpretive or creative nature (this activity must still be reserved for humans).

How Intelligent Document Processing Works

To efficiently and automatically process an absolutely varied and heterogeneous quantity of data and documents, IDP systems follow three macro-phases:

1. Data collection: the first action carried out by the IDP system is the intelligent acquisition of documents. If your documents are in paper format, you will need to initiate scans to convert the paper documents into digital images. Using technologies such as AI, ML, OCR and ICR, relevant data will be captured from paper documents.
2. Data extraction: the second phase involves the extraction of relevant information extracted from the documents acquired in the first phase, or from other sources already present in digital format, using a pattern matching tool, such as the presence of regular expressions. Artificial interpretation of information is essential to successful data mining. Since AI is only as intelligent as its training, the system must be able to locate and classify all the expected information in a document.
3. Data validation: To ensure the accuracy of the processing results, the extracted data is subjected to a series of automatic or manual validation tests. To this end, IDP systems use external databases to verify information. Any information that does not match is highlighted so that there can always be human inspection and manual correction of the data.
4. Data integration: Collected data is compiled into a final output file, usually in JSON or XML format. APIs are used to send the file to a business process or data repository. The collected information must then be stored or transmitted to other systems for processing by automated business processes. Many IDP solutions on the market provide interfaces that connect to CRM, ERP and DMS systems, enabling the automatic backup, organization and protection of data extracted into these systems.