Modern companies have to process vast quantities of invoices, letters and other documents. The problem: senders do not adhere to uniform rules in any way. Central information such as invoice or transaction numbers is always in a different place. This makes the Document classification to a Sisyphean task. The solution: "AI Data Extraction" - data extraction with modern AI.
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What is AI Data Extraction?
This type of data extraction is not to be confused with a so-called data lake, which is merely the prerequisite for the use of AI. AI Data Extraction goes beyond the mere schematic collection of data. In doing so, AI performs superhuman tasks. It is adaptive, Structures the raw data and meets accurate predictions for the optimization of processes. For companies, this service means real added value, as AI increases data quality and reduces costs.
With AI Data Extraction, employees can save themselves the trouble of manually entering or copying data from documents in the future. AI will do it for them. In addition, data extraction software minimizes the risk of human errors during data entry in a digital world.
AI is more flexible than rule-based data extraction software
Before the use of AI, data extraction processes were template-centric. This meant that employees had to create a template for each group of documents with a similar structure. Companies must manually configure such an input management system. Agents specify how they want to transfer numbers and data from documents into target systems by following certain rules.
But that quickly reaches its limits with today's data volumes. Companies must daily process hundreds of pages and deal with many different document classes. The consequences: Precision and recognition rate decline. The system functions less and less automatically. The effort required for system maintenance and post-processing increases. The only solution is to switch to processes with Machine Learning: Data extrusion software must become intelligent.
Data extraction tools help with automation
Data extraction with AI goes beyond standard functions such as optical text recognition (OCR) by far. In addition, there is a Natural language understanding, Natural Language Processing (NLP). This enables an API (Application Programming Interface) to correctly understand, evaluate and assign data. AI algorithms are used, which can read texts and similar to a human brain be able to interpret.
However, humans must first configure such a platform for customer requirements. The AI has to be "learned", so to speak. The AI learns through a wealth of training data and defined rules. Employees make corrections in the process so that the machine applies the rules better and better. To prevent performance from dropping, powerful AIs even manage to learn from their own mistakes and thus become always smarter.
The secret of this wonder weapon is called "machine learning". The AI uses the clerks' corrections to improve itself through unsupervised learning. Employees can sit back and watch as the AI learns, but is still oriented toward humans.
What are the benefits of AI Data Extraction?
To keep up in the market, companies are increasingly relying on systems with AI. Classic automated processes with OCR and ICR (Intelligent Character Recognition) are no longer sufficient to Competitive advantage to work out. Data Extraction has several advantages:
- better data quality
- Reduce costs
- Implement processes faster
- Automation of data entry
Why is data extraction with AI important for businesses?
On average, 20 % of a typical digital company's database is full of unorganized data. Such "dirty data" impairs business success. AI Data Extraction reduces errors, Brings order into the data and leads to more accurate results.
Time is money: When team members Less errors have to correct when extracting data, it saves a lot of time. Instead, they can focus on other tasks, which increases revenue. Important decisions can be made much more efficiently through data extraction with AI, as employees no longer have to search for the necessary information themselves.
Modern data extraction software organizes documents easily
Software tools that capture unstructured data and make it machine-readable are important for data extraction. First, the AI software the document at different data points. This creates Structured digital data, which tell the system where to look in the documents. Now the system knows what kind of data the company wants to extract. The automatic data extraction can begin.
After the extraction of the data has begun, the AI can use the Automate process. To do this, the API must collect enough documents and, using machine learning Learn intelligentlyhow to extract the data. Humans hardly need to check this process.
Team members can then easily forward the organized documents, resulting in faster business decisions leads. This puts an end to the eternal search for data. The data extraction process can be fully tailored to individual business requirements.
Modern Data extraction software is even able to work efficiently with different languages to handle. To do this, the human must show the machine sample documents in that language. When the computer understands the content of documents, it can also recognize contextual nuances of the corresponding language. This technology can thus categorize and organize information in documents much better.
ETL is a process in which data is integrated into a database or data warehouse. Data extraction is the first step in the ETL process. This selects the data from the source systems and prepares for the transformation phase. After that, the data must be transformed into the format of the target database and uploaded there.
Data extraction is the process of collecting or retrieving different pieces of information from a variety of documents in order to automatically organize, store, and thus process them in a structured manner. In order to recognize the text in images or scanned documents, OCR, spelled out optical character recognition, is used. With today's AI technology, most documents can then be automatically processed and thus converted into structured data. Therefore, the quality of data extraction by AI is a special achievement for the automation of back-office activities.
Big Data holds a lot of potential data and insights that need to be discovered by the business. You can only unlock its value if you have the right technology and tools in place. This includes the API tools that allow you to quickly and efficiently extract data from your sources. For any organization, "time is money." Therefore, you should consider API tools for Data Extraction that can help you improve your workflow and save time. A Data extraction API, when used properly, can save your team time and allow them to focus on more important tasks.
OCR scans the data in the PDF file and identifies exactly which field it belongs to. The field name and the corresponding data are matched and extracted from it. For example, a passport has a name, passport number, date of birth, date of issue, expiration date, and citizenship as some basic fields. The data from these fields are scanned, identified and matched if there are multiple passport copies, regardless of which country's passport it is. So if one country's passport template/structure is different from another, it doesn't matter as the data is extracted from the field names and matched.