FastApp Group – How to extract data from unstructured documents

Document data extraction

In today’s data-driven environment, extracting meaningful information from documents is a common challenge many businesses face. This challenge intensifies when dealing with unstructured documents – a realm where traditional data extraction methods often stumble. Unstructured documents, such as emails, invoices, contracts, and even handwritten notes, do not follow a specific format, making the extraction process intricate. However, innovative technologies are now simplifying this process, allowing for the efficient retrieval of structured data from these complex sources.

Understanding Unstructured Data

Before diving into the extraction process, it’s crucial to understand what unstructured data entails. Unlike structured data, which resides in fixed fields within a record or file, unstructured data is the information that does not have a pre-defined data model. This includes text files, emails, social media posts, and documents in various formats (PDF, Word, images, etc.), all of which can hold valuable information for your business.

The Extraction Process

The extraction of data from unstructured documents involves several steps, designed to convert the chaos into a structured format that can be easily analyzed and utilized.

  1. Document Collection: Gathering all the documents from which data needs to be extracted. This step can involve both digital and physical documents.

  2. Pre-processing: In cases of physical documents or images, Optical Character Recognition (OCR) technology is used to convert them into a machine-readable format. For digital files, this step may involve format normalization.

  3. Data Identification: This involves defining what information needs to be extracted. Users can specify the details, such as dates, amounts, names, or any specific data points relevant to their needs.

  4. Extraction: Leveraging advanced algorithms, the technology then extracts the specified information from the documents. This step does not require pre-training of models, which significantly speeds up the process and enhances accuracy.

  5. Validation and Structuring: The extracted data is then validated for accuracy and structured into a usable format, typically a table, which can then be easily integrated into databases or other applications.

Technologies Behind the Scenes

The heart of efficient data extraction lies in the technology used. Modern solutions employ sophisticated algorithms that can handle a wide range of document types and formats, from PDFs and Word documents to images and scans. These technologies do not rely on pre-defined templates or extensive training, making them capable of processing hundreds of documents in minutes with high accuracy.

Integration and Usage

Once the data is extracted and structured, it can be downloaded or directly integrated into existing workflows or databases via APIs. This seamless integration capability allows businesses to automate their data processing tasks, reducing manual effort and increasing efficiency.

The Advantages

Efficiency: Automates the extraction process, significantly reducing the time and effort required to process documents.

Accuracy: Advanced algorithms ensure high accuracy, minimizing errors associated with manual data entry.

Flexibility: Can handle a wide range of document types and formats, offering businesses versatility in processing their documents.

Scalability: Capable of processing large volumes of documents quickly, catering to businesses of all sizes.

Discover revolutionizes the way businesses handle unstructured documents. With its no pre-training technology, it offers swift and accurate data extraction from various document types, including PDFs, emails, and even handwritten notes. The platform’s ease of use is enhanced by a powerful API, facilitating seamless integration into existing workflows or databases. By allowing users to test the service with the first 50 pages free and employing a pay-as-you-go model, makes advanced data extraction accessible to all. This enables businesses to significantly cut down on manual processing, improving efficiency and data management without the complexity. If you want to create your own document parsing template, check for free the Document Parsing API.


Extracting data from unstructured documents no longer needs to be a daunting task. With the right technology, businesses can easily convert their documents into structured, actionable data. This not only streamlines workflows but also provides insights that were previously difficult to access, offering a competitive edge in today’s data-centric world. Whether you’re a freelancer, part of a small team, or a large enterprise, harnessing these technologies can significantly impact your operational efficiency and decision-making processes.

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