Intelligent Data Processing

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Intelligent Data Processing

Intelligent Data Processing(IDP) is a new technology that can recognize and extract useful data from various documents, such as scanned forms, PDF files, and emails, and transform it into the necessary format automatically.

Artificial intelligence (AI) is used to power IDP’s sophisticated technologies, which include machine learning, deep learning, optical character recognition (OCR), and natural language processing (NLP).

IDP is capable of automatically capturing, classifying, and extracting useful data from complex and unstructured documents in an orderly manner.

IDP has been associate with many other names as it has emerged with different technologies:

  • Intelligent Document Processing (IDP)
  • Intelligent Data Processing (IDP)
  • Intelligent Data Capture (IDC)
  • Intelligent Data Extraction
  • Cognitive Document Processing
  • Enterprise Cognitive Computing (ECC) Application

The three Components of intelligent document processing

Machine learning, optical character recognition, and robotic process automation are three cornerstones when it comes to processing documents in a new, smart way. Consider intelligent document processing as a living organism to better grasp how magic works. OCR can be viewed as the “eyes,” machine learning as the “brain,” and RPA as the “arms and legs” in this context.

  • Optical Character Recognition(OCR): OCR is a narrowly focused technique that can recognize handwritten, typed, or printed text among scanned images and convert it into a machine-readable format. OCR as a stand-alone solution just “sees” what’s on a document and extracts the textual portion of the image, but it has no understanding of the meanings or context. It is for this reason that the “brain” is required.
  • Machine learning(ML): Machine learning is an application of science that focuses on developing algorithms and training models on data so that they can analyze fresh data inputs and make decisions on their own. IDP mainly relies on machine learning-driven technologies such as:
    • Computer Vision(CV): Deep neural networks are used in Computer Vision to recognize images. It recognizes patterns in visual data, such as document scans, and categorizes them appropriately.
    • Natural Language Processing (NLP): is a method of locating and interpreting language elements in documents, such as discrete phrases, words, symbols, and so on, and performing a linguistic-based document summary.
  • Robotic Process Automation(RPA): RPA is a technology that uses software bots to automate repetitive corporate processes (robots). Working with structured data has shown to be a successful application of the technology.
    • RPA software can be set up to collect data from a variety of sources, process and manipulate data, and interface with other systems.
    • Most crucially, because RPA bots are often rule-based, any changes in the structure of the input will prevent them from doing a task.
    • As a result, most IDP solutions are integrated with different phases in the document processing cycle on RPA systems that involve the usage of OCR technology. As a result, document-driven processes can be fully automated.

The reason to have IDP in your Company

  • IDP relieves your staff from repetitive data entry, decreases errors, and gives them more time.
  • IDP provides structure and order by employing accurate data and automating procedures.
  • IDP gives your company a lot of flexibility and the opportunity to scale rapidly and easily.
  • IDP reduces costs while optimizing process cycle times.

IDP Performs following Functions

Intelligent document processing mainly performs collecting Data, pre-processing, classifying documents, data extraction, validation, and integration.

  1. Collecting Document: Document collecting is the process of collecting different types of data sources either in paper or electronic content format. . IDP solutions are currently integrated with hardware such as scanners to digitize paper/handwritten documents and speed up scanning procedures. Built-in connectors present in IDP accept documents in digital forms, such as PDF, Word, and Excel files, emails, and so on.
  2. Document Pre-Processing: Improves the quality of the scanned or captured documents, the following steps are included in this process:
    • Deskewing: correcting the angle of the scanned image skew;
    • Decreasing noise: getting rid of background spots, interfering strokes, uneven contrast, and other textual and non-textual noise;
    • Binarization: converting the grayscale scanned document image into black and white; and
    • Cropping: removing the unwanted outer areas from an image.
  3. Document Classification: Document classification is done based on their structure and content, IDP may automatically divide documents into several groups. Advanced IDP solutions (such as Infrared) may receive many documents in a single image and separate and classify them automatically so they can be directed to the appropriate task queues. This automation speeds up document processing and minimizes or eliminates the human labor that might stymie intelligent automation.AI-driven document classification can be performed
    • Based on image patterns, with the help of computer vision algorithms in the case of scans or document pictures; and
    • Based on the textual content, using NLP techniques in the case of electronic documents.
  4. Document Extraction: The data extraction process is the most critical phase of the IDP, which needs trained and skilled professionals. First, IDP relies on the OCR and extracts the data from the images, scanned copies, and PDF files and then converts it into a readable digital format. Next with the help of NLP technologies, IDP will decide what kind of data to extract, such as dates, figures, and names. ML-trained models can also be used to make data consistent (for example, $5 instead of $5), correct common misspellings, transform data into a standard output format, and much more.
  5. Validation: External databases and pre-configured lexicons are used by IDP platforms to evaluate data extracted from documents, ensuring data accuracy and integrity. This method not only ensures data quality but also ensures that data is captured in the correct format and ready for instant use. In most cases, a HITL (Human-in-the-Loop) machine learning framework is used to validate data, with incorrect data being directed to humans for assessment and correction. The validation model can learn and increase its accuracy over time using this approach.
  6. Data Integration: After all of these steps have been completed, the data can be pushed into the company’s IT systems using APIs. Databases and document repositories, both local and cloud-based, are included. RPA tools can connect to a destination system API and transfer the data there since the data is in a standardized, structured format.

Realtime Usecase of Intelligent Document Processing

Healthcare Department:

To maintain Medical forms and patient records:

  • Healthcare departments may have lots of data and records that are very much important because these records reflect the effectiveness of provided care and fatal consequences.
  • You may automatically retrieve useful information from several healthcare documents, including doctors’ notes, vaccine permission forms, government COVID testing forms, and health status data, to mention a few, with the IDP solution in place.

Finance Department:

  • Enabling IDP can substantially speed up the loan application process. It can evaluate documents uploaded to a company’s website automatically, extract essential data, cross-check it against existing databases, and route validated submissions to the appropriate systems. As a result, the loan approval procedure is less complicated, and the quality of service is increased.

Insurance Department:

  • Some insurance claims are printed by the machine, some are hand-written and some include associated data such as damage images. Manually reviewing these claims is a time-consuming and difficult operation. Insurance firms can automate claim processing with the correct IDP system in place. As a result, pertinent data is extracted from a variety of claims and supplied into a claims processing system downstream.

The Difference between IDP and OCR

  • IDP handles complicated, unstructured, and handwritten documents with ease. IDP is the key to document variance. At the tiniest variance, traditional OCR fails terribly.
  • IDP doesn’t require templates, learns from new data, and improves over time. Traditional OCR relies on templates and is incapable of self-learning.
  • Only extremely precise and below-established confidence levels do IDP rely on people for verification. There is always room for improvement with standard OCR.

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