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.
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.
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.
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 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 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 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
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 point-and-click platform and is suitable for non-technical people.
Parseur uses ML, Zonal OCR, and Dynamic OCR to efficiently extract text and process them within seconds. Built-in templates are available ready-made for specific industries 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.