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Data Capture in Practice. How Is Automated Data Extraction Useful?

Category: Blog, IT solutions
Data Capture in Practice. How Is Automated Data Extraction Useful? | Blog Archman

It is often necessary to rewrite data from a document to an electronic form during office work. These may be, for example, information about the client, purchase and bank account number from an invoice, elements of an order, or information about a potential candidate from a received CV. Rewriting data each time is a tiring and tedious activity. So, it’s a great idea to assign this work to machines.

Data capture technology opportunities

Data capture is a very broad term. We use it when talking about the recognition of the objects or shapes in an image, specific patterns of information, or key phrases. It is sort of the ”eyes” of a computer program that tries to replicate a human being is looking at a photo or document while searching for information on it. This technology can help in many ways. For example, we can use it in facial system recognition development, fingerprint recognition, or programs that drive autonomous vehicles, or textual or tabular data extraction.

How efficient is data capture technology?

Data capture effectiveness is quite hard to measure in general terms because we use different metrics for different types of recognized data. Besides, we expect various accuracy levels considering circumstances. Security systems, such as fingerprint readers or face recognition algorithms must have virtually 100% efficiency. Systems that recognize traffic signs, lanes, or traffic lights also require extreme accuracy. However, in the case of data rewriting from a document that will be later approved by a human – minor typos are acceptable (often having nothing to do with data capture itself, but with the quality of the OCR mechanism).

It is also common practice not to return unlikely data and inform the user when content needs to be added. Based on this human action, high-quality systems such as NAVIGATOR will learn and perform the correct activities on subsequent documents using the ‘knowledge’ gained from user actions. The best document capture systems have an efficiency of over 85-90%. They are also capable of relevant data validation, such as IBAN, VAT ID, or TAX ID numbers. This functionality makes a significant difference and increases accuracy compared to manual completion by the user.

In which areas is data capture currently used?

All the possibilities for using data extraction technology are virtually incalculable. This technology can be used in the manufacturing industry (while improving, analyzing, and monitoring machine operations), automotive sector (in autonomous vehicles, driver assistance systems, DVRs), as well as in security technologies (facial recognition, fingerprint, retina, etc.), medicine (to diagnose diseases, such as cancer), or simply in office work automation (document data rewriting and validation). There are for sure many undeveloped areas where data capture can support humans.

Data capture technology development perspectives

Data capture technology will certainly find its way into many areas of our lives. We should be happy about that because it will make everyday life much easier, safer, and free of unnecessary work. The effectiveness of this technology will also increase over time. Not only the larger data sets are collected, but also the existing models will learn through interactions with users. New algorithms are still being researched at universities, while the business sector is not lagging either. They are not merely implementing academic solutions but are looking for new scenarios for the artificial intelligence algorithms’ usage.

Just think of the biggest market players, such as Amazon and Google. These giants are constantly adding new data capture services to their portfolio. However, many companies find the prices of these services discouraging, as well as their lack of adaptation to the European reality. Another disadvantage is the relatively long operation time and the need to send data via the network. Fortunately, effective solutions come from Poland. Module AI in NAVIGATOR is the perfect proof for this statement. It is fast, fully adapted to Polish accounting documents, and available on-premises.

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Data Scientist in WeDeliverAI team (Archman), enthusiast of mathematics, statistics, and new technologies. Finished AGH University of Science and Technology with a degree in Computer Science and Econometrics; Machine Learning module graduate at WSEI Programming School. In everyday work, he prepares algorithms based on machine learning, used in NAVIGATOR system – solving problems regarding process automation.

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