What is text extraction? (Techniques and Use cases)

Text extraction refers to the extraction of text from documents, images or scanned PDFs. It is an essential part of the data analysis process and is used to gain insights from large amounts of text data.

In this article, we will discuss how text extraction works, the various text extraction techniques, and some use cases.

What is text extract?

Did you know: 2.5 quintillion (10^18) bytes of data are generated every day?

With that amount of data, businesses can gather insights about their customers and products providing them with a competitive edge. However, the key is to analyze and process those data effectively with zero errors. And, this is where text extraction comes in and plays a major role in the data processing.

Text extraction can be done manually, by staff going through the text and interpreting it, or it can be done automatically using several text extractors.

What is the difference between text extraction and text mining?

Text extraction helps to retrieve specific information while text mining tries to identify patterns within extensive sets of data. An example of text mining is recognizing the emotions of people (positive, negative, neutral) in comments.

Challenges of manual text extraction

Manual text extraction works well if you have a single document to extract from with the same format. But, if you have to extract data from hundreds of PDFs with different layouts, then manual extraction can become challenging.

Time-consuming

It takes time to go through different documents and extract the text correctly. For example, if you are a food delivery company, time is of the essence. The moment you receive an order confirmation, the customer's details have to be retrieved quickly and shared with your team.

Prone to errors

Undoubtedly, manual text extraction results in many human errors which go unnoticed. Imagine the wrong food orders being delivered to one of your customers.

Thanks to automated text extraction, companies can now extract large volumes of data within seconds and thus, reducing manual labor and saving costs.

How does automated text extraction work?

Text extraction is the first step in the "Extract-load-transform (ETL)" process. The first step in the text extraction process is to identify the data that needs to be extracted. For example, if your document is an invoice, then data fields such as the "invoice number", "invoice date", "customer name" and "table fields (description, quantity, unit price, discount, total price)" will be identified.

Once the data has been identified, the text extraction algorithm will use different techniques, such as natural language processing and machine learning, to extract the data.

The text extraction process can be summarized in the following steps:

The document is first categorized (for example, is it an invoice, an order confirmation, or a BoL document?).
The meta fields are identified (for example, full name, number, date, address or price).
Data is extracted as per specific requirements.

Text extraction techniques and methods

There are several text extraction techniques used to extract data from text documents, such as optical character recognition (OCR) or natural language processing (NLP).

Let's look at those methods in more detail.

Machine learning

ML is ideal for this purpose because it can learn from examples and then generalize that knowledge to other documents. This means that once you have trained a machine learning model on a specific set of documents, you can use it to extract information from any other document in your corpus.

OCR

This involves converting images of text (such as scanned documents or images of text on a screen) into machine-readable text. OCR software uses pattern recognition algorithms to identify and extract the text from the image.

NLP

NLP uses algorithms to analyze and understand the meaning and context of the text. NLP techniques can be used to extract information from unstructured text, such as extracting names or dates from a document.

Regular expressions

Regular expressions involve using a set of rules or patterns to identify and extract specific pieces of text from a larger body of text. Regular expressions are often used to extract specific types of data, such as email addresses or phone numbers, from a document.

Applications of text extraction

Text extraction has a wide range of applications in various industries and fields. Some common applications of text extraction include:

Real estate

Real estate agents receive hundreds of real estate leads daily from different real estate platforms Zillow, Trulia and third-parties platforms. Extracting text automatically will help close real estate deals quicker.

Learn more about automating real estate processes

Financial & Legal

Text extraction can be used to extract specific information from legal or financial documents, such as contracts or financial statements, to facilitate analysis and decision-making.

Food ordering & delivery

Automated text extraction can speed up the food delivery process as data will be extracted faster and can be sent to shared Google Sheets automatically.

Automate your food ordering process and create your DoorDash API

E-commerce

Managing an online store on Shopify or WooCommerce means that you will receive all your orders digitally. With automated text extraction, you can create a workflow process between Shopify and HubSpot CRM, for example.

Parseur: A powerful text extraction tool

Parseur is a text extraction software that automatically extracts text from different documents. What differentiates Parseur from other tools is that it has a powerful AI engine and is suitable for non-technical people.

Try out our powerful document processing tool for free.

Parseur uses AI, Zonal OCR, and Dynamic OCR to efficiently extract text and process them within seconds. The AI tool is trained to extract data from different use cases such as food delivery, invoicing, or Google Alerts.

With the Parseur app, you can also integrate hundreds of other applications with your extracted data.

Text extraction helps to gain real-time data

With Google handling over 1.2 trillion searches every year, the volume of data keeps increasing and changing. Extracting accurate data is the key to understanding consumer behaviors and making better informed data-driven decisions.

Last updated on June 30th, 2026

Ready to automate your
document data extraction?

Start free in minutes and see how Parseur fits into your workflow.

No model training required

Automates data entry from any document

Scales from point-and-click to API

Frequently Asked Questions

Common questions about text extraction, how it works, the techniques involved, and how to automate it.

Text extraction is the process of retrieving specific text and data from documents, images, or scanned PDFs so it can be used for analysis or downstream workflows. It is a core part of data processing and helps businesses turn unstructured content into structured, usable information. Text extraction can be done manually by staff or automatically using software that reads and pulls out the relevant fields.

Text extraction retrieves specific pieces of information from a document, such as an invoice number or a customer name. Text mining, by contrast, analyzes large sets of data to identify patterns and insights, such as detecting whether comments express positive, negative, or neutral sentiment. In short, text extraction is about pulling out defined data points while text mining is about discovering trends across many documents.

OCR, or optical character recognition, is a text extraction technique that converts images of text, such as scanned documents or screenshots, into machine-readable text. It uses pattern recognition algorithms to identify and extract characters from the image. OCR is essential for processing paper documents and scanned PDFs that do not contain selectable digital text.

Text extraction is used across many industries, including real estate, finance, legal, food delivery, and e-commerce. Real estate teams use it to process leads from listing platforms faster, while finance and legal teams use it to pull key details from contracts and statements. Food delivery and e-commerce businesses rely on it to capture order data automatically and route it to spreadsheets, CRMs, or other tools.

Automated text extraction can process large volumes of data within seconds with far fewer errors than manual entry. Tools like Parseur combine AI with techniques such as Zonal OCR and Dynamic OCR to read documents reliably across different layouts. For added confidence, Parseur offers an optional manual review step where a person can check and correct extracted data before it is exported.

Automated text extraction works by first categorizing the document, such as identifying whether it is an invoice, an order confirmation, or a bill of lading. The software then locates the meta fields that need to be captured, like names, dates, addresses, and amounts, and extracts the data according to specific requirements. It typically relies on techniques such as optical character recognition, natural language processing, and machine learning to read and interpret the content.

The main text extraction techniques are machine learning, optical character recognition, natural language processing, and regular expressions. Machine learning learns from example documents and generalizes that knowledge to new ones, while optical character recognition converts images of text into machine-readable text. Natural language processing analyzes the meaning and context of unstructured text, and regular expressions use rule-based patterns to capture specific data like email addresses or phone numbers.

Manual text extraction is time-consuming and prone to human error, especially when handling large volumes of documents with different layouts. Going through hundreds of PDFs by hand takes significant time and can delay urgent processes like order fulfillment. Mistakes such as mistyped figures or missed fields often go unnoticed, which is why many companies switch to automated extraction to save time and reduce costs.

Parseur is a text extraction tool that automatically extracts text from documents, emails, and PDFs without requiring any code. Its built-in AI extracts the requested fields from any layout, so there is no need to build a separate template for each format or vendor. Parseur also lets non-technical users connect the extracted data to hundreds of other applications and integrations.