What is Intelligent Data Extraction?

In today’s digital world, structured and unstructured data resources are widely available for businesses. This data comes with the power to open insights, improve decision-making, and create innovation. The efficiency, accuracy, and scalability constraints of traditional data extraction methods frequently prevent them from fully realizing this promise. This is the time when intelligent data extraction comes in as a unique transformative solution.

Using advanced techniques like NLP, machine learning (ML), etc., AI document extraction helps to extract accurate and efficient information from different data sources. This will increase your productivity and improve your workflows.

‍Why is Data Extraction Technology Considered "Intelligent"?

For example, consider OCR engines. Training the OCR model to recognize that the transaction references on a given bank statement are to the left of the transaction amount is quite simple. However, basic visual technology is incapable of deciphering the significance of the data it records.

By contextualization, on the other hand, intelligent data extraction actively comprehends minute details in the material on the page. For instance, the algorithms can differentiate between ACH credits and debits on a bank statement, so accurate data can be recorded even from complex tables.

Limitations of Template-based OCR

• Depending on the quality of the documents 

The quality of the image input supplied to the engine directly relates to the quality of text recognition and extraction. For instance, the accuracy significantly drops when the character height is fewer than 20 pixels.

• Using templates and following rules

Templates and guidelines must be used for traditional OCR. Strict guidelines must be followed while programming the engine to accept data from the appropriate fields and lines. As such, it struggles with unstructured documents and cannot handle a wide range of them. 

• Poor automation potential

Conventional OCR is limited in its automation choices by its reliance on templates and rules. For example, a  rule would be required for every data field if you wanted to extract structured data from invoices. There are many limitations since, as you know, invoices can take on a multitude of documents.

The OCR engine would require more resources and training data to support more rules. The traditional method may result in a major bottleneck since there will always be more regulations to develop. 

• Expensive

The cost of traditional OCR can increase significantly when more rules and algorithms are needed to increase accuracy. A high-quality outcome is not always guaranteed by the development of these rules and algorithms, as picture input quality is a contributing component.

• Does not handle high volumes of a variety of document types efficiently

Standard OCR often yields fairly accurate results when scanning simple documents with few modifications. However, many companies have a variety of documents that need to be handled by their systems.

The complexity increases with the diversity of the documents. The standard OCR engine can't keep up with a variety of texts as it is trained using limited templates. 

Manual Processing vs Optical Character Recognition vs Intelligent Data Extraction

How Does Intelligent Data Extraction Work?

Intelligent Data Extraction (IDE), is the process of extracting data without human intervention. It works similarly to how humans identify the text and characters. Humans read the text and manually enter the extracted information into a system. This is time-consuming, and errors can occur due to manual data extraction. Intelligent data extraction will help to save time and make the work easier.

Intelligent data extraction processes the following steps: 

1. Pre-processing images

In intelligent data extraction, image pre-processing is the initial stage which will make sure that the input is prepared for precise extraction. At this point, the following procedures take place:

• De-skew:

‍The inserted picture has to be de-skewned first. De-skewing will fix any anomalies in scanned or recorded photos, and text must be put immediately for appropriate processing.

• Binary

To convert grayscale photographs to binary format, use this simple graphics software. By using binary, you can quickly see the context and turn black pictures into white.

• Zoning:

A de-diagonal procedure is typically used to separate the picture into zones or sections. By splitting apart the page, the algorithm may concentrate on specific regions of interest, which improves accuracy and performance.

• Normalization:

Normalization will bring the size and quality of the photos into better balance. By altering the contrast, shape, and light in this step, the content will become more clear.

2. Document categorization

Data categorization is done after the pictures are transformed in order to increase the accuracy of feature extraction. At this level, documents are classified based on design, content, or intended usage using AI document extraction.

Classification guarantees that every document is directed to the appropriate processing pipeline, facilitating intelligent data extraction and validation optimization. For instance, the system employs distinct AI algorithms for information extraction that are suitable for currencies and contracts.

3. Character Recognition

This is a crucial procedure. Sections, tables, subsections, and fields can be found in a design or document. Important colors or characteristics can be found inside them when they are separated. At this point, two approaches are employed.

Matrix correspondence: This is the procedure for matching a column matrix database to individual columns. OCR engine looks for every match pixel by pixel

Feature recognition: This technique may be used to recognize text and character properties in images. The collection that is already available has already been compared to the form, height, kind, lines, and structure.

4. Post-production of the output

After that, the retrieved data is refined and improved by post-processing. Resolving ambiguities, fixing mistakes, and enhancing the data's general quality are all included in post-processing. We'll employ methods like grammatical analysis and spelling checks to make sure the material we know is accurate and contextually relevant. This phase of intelligent data capture aims to deliver dependable, superior data that you can utilize with ease to inform your decisions.

Benefits of Intelligent Data Extraction

• Lowers expenses of operations

IDE saves money as well as the time. Operational expenses related to the errors caused by human data entry will be reduced by using the AI document extraction. IDE makes the process faster and this will reduce the possibility of errors occurring during the manual data entry.

• single point of capture

Intelligent data capture learns to identify various document kinds in the same location as sensitive information is gathered and sourced. It functions better the more data it processes.

• Increased protection

Only those who examine and validate the data may access the material. It encrypts the input data before recording and safely storing it to avoid data loss or overflow.

• Enhanced adherence

It offers high-quality, precisely segmented and labeled data. Furthermore, the data audit trail guarantees adherence to legal and regulatory obligations.

• A  well done act

It supports department-specific users and procedures on a single platform. It therefore makes access, authentication, and intelligent data collection easier.

• Increased production

Data devoid of errors is produced by the method, which removes tiresome labor. You may concentrate on other, more vital duties while automatic data collecting handles the business.

Intelligent Data Extraction at Infrrd

The intelligent data platform of Infrrd provides different creative approaches for the intelligent data extraction process. By the use of artificial intelligence and machine learning, Infrrd’s IDP maintain unstructured data from different sources such as documents, images, and emails with ease. This intelligent data platform recognizes and extracts important information from different sources using intelligent document processing (IDP). Infrrd IDP will help you tp sove your problems and make decisions based on the information. 

Infrrd's IDP give guarantee about 100% accuracy and productivity from the extracted data.  Infrrd maintain the consistency and integrity throughout the extraction process. By incorporating the extracted data into their current processes, organizations may swiftly accelerate operations and meet business objectives. 

IDP is Better than OCR

OCR is typically the first thing that springs to mind when someone says the word data extraction. The go-to option for data extraction for the past few years has been standard OCR systems. However because their main goal is to transform printed or handwritten text into a digital data format that can be read by machines, optical character recognition (OCR) systems are not without problems.

A significant amount of potential is wasted on simple data extraction without the intelligence to interpret what the data means. The emergence of neural networks and algorithms for computer vision and natural language processing, which are employed in contemporary IDP solutions, is advantageous to organizations because of the rapid advancement of technology.

With the ability to handle millions of document variants, such as invoices, receipts, loan papers, and insurance documents, modern IDP systems allow intelligent data extraction without the need for template creation. IDP leaders with a solid commitment to intelligent data extraction include Infrrd. Businesses used to rely on their people's resources and knowledge. AI document extraction naturally becomes a crucial component for a firm as the corporate world nowadays depends on data analytics to obtain superior business insights.

IDP has the ability to extract using the information extraction AI valuable information for your company from the document's visual and textual components. One significant distinction between OCR and IDP systems is this. While IDP systems are intended from the start-up to handle both sorts of material, OCRs are not meant to handle visual aspects. To extract intelligent data from each of these content categories, Infrrd's platform makes use of computer vision, deep learning, machine learning, and natural language processing.

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