Processing and analyzing large amounts of text data is a challenge for organizations, and one that Regex Generator can help with.
To achieve effective and accurate results, the models for the Named Entity Recognition (NER) be adapted to the respective business context. However, extensive training data is required for this. Errors in the training data can have serious consequences and affect the results.
In this article, we show Python developers how to use the Konfuzio SDK to create training data using the Python Regex Generator and digitize their documents more effectively.
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Introduction
Maybe you've heard of regex, but don't know exactly how to use it or what it is. Or you've already tried some online tools to generate your regex expressions, but didn't get the results you wanted.
Then you have come to the right place! In this post we will show you how to use a regex generator to work more effectively and efficiently can. We will explain not only what Regex is and how it works, but also which Benefits a custom regex generator has and why it is worth taking the time to create a own regex generator to create.
Many online tools offer free regex generators that are quick and easy to use, but often don't deliver the results you want. With a custom regex generator, you can define your own rules and tailor them to your specific needs for precise and accurate results.
We will also show you how to create and use your own regex generator with the Konfuzio SDK. This will help you understand your texts better and work more effectively in your daily work.
So, before you go looking for a free online regex generator, be sure to read this blog post and learn how to create your own custom regex generator that will give you exactly what you need!
What is a Regex Generator?
A regex generator is a code library capable of extracting structured information from a text.
In this context, the Python Regex Generator is often used for Named Entity Recognition, which is considered part of the Document Understanding domain. However, entities such as names, addresses or amounts are only recognized if you configure and train the generator accordingly.
Application examples for companies
Python Regex Generator is a powerful tool that helps organizations digitize and effectively evaluate their documents. Here are some application examples:
- Payroll: Companies can use the Python Regex Generator to evaluate the various factors in payroll reports, such as the amount paid out, the social security number, or the tax bracket.
- Certificates of Merit: By reading out data such as the gross and net salary, the start and end of work or the number of overtime hours, companies can automatically digitize and evaluate their employees' earnings statements.
- Tax settlements: Python Regex Generator can help companies extract important data from tax statements such as tax rates or tax refund amounts.
- Identity cards and driver's licenses: Companies can use the Python Regex Generator to extract data from ID cards and driver's licenses, such as the name, date of birth or driver's license class.
How does the Regex Generator work?
In order to use the Regex Generator, various labels must first be defined. These labels are programmed to read and extract specific positions in the document.
For example, a label for the extraction of monetary amounts on an invoice can contain the following rule: Spaces before the label, followed by numbers that you separate with a comma.
For each piece of information to be extracted from a document, a label must be defined using a code. If several regexes run side by side in a document, all relevant data can be extracted from the document.
Free alternatives for Regex Generator
There are several free regex generators that can be used for simple extraction tasks. Here are five such generators:
- RegExr: RegExr is a free online regex generator that offers a wide range of features. You can create your regex expression step-by-step while checking live if the regex is applied to the text. The user interface is intuitive and offers a variety of troubleshooting features.
- RegExLib: RegExLib is an online community for RegEx developers. You can access a large library of RegEx expressions and customize them for your own extraction tasks. RegExLib also provides a forum for discussing RegEx topics.
- Regex101: Regex101 is a free online regex generator that provides a simple interface for creating regex expressions. You can create your regex expressions step-by-step, checking live if the regex is applied to the text. Regex101 also provides a library of RegEx expression examples.
- RexEgg: RexEgg is an online regex generator that provides an extensive library of regex expressions. The library contains expressions for a variety of use cases, including email addresses, URLs, and IP addresses. RexEgg also provides a set of tools and resources for working with regex expressions.
- RegexBuddy: RegexBuddy is a paid RegEx development platform that provides a comprehensive suite of tools for creating and editing RegEx expressions. You can create your regex expressions step-by-step while checking live if the regex is applied to the text. RegexBuddy also provides a library of RegEx expressions and a variety of troubleshooting features.
Although these free regex generators can be useful, they also have some disadvantages compared to the Konfuzio SDK.
For example, they may not offer the same depth of features and tools as the Konfuzio SDK. They may also not be as user-friendly and may require more expertise to use effectively.
In addition, they may not provide the same reliability and accuracy in extracting information as the Konfuzio SDK, which we developed specifically for business applications.
Konfuzio SDK
The Konfuzio SDK is a comprehensive platform that provides an easy and intuitive way to create training data for NER models. With the help of the SDK, Python developers can effortlessly define custom labels for their documents and use the Python Regex Generator to automatically read out the relevant information.
To be able to use the Konfuzio SDK, you must first test all relevant labels using training documents. In the process, the AI learns by providing information and can work independently. If errors occur or positions are not read correctly, developers can manually train the AI to ensure correctness.
The Konfuzio SDK also provides a user interface for creating and managing labels. Here, developers can train the AI to recognize any possible position by defining different labels such as first name, last name, net earnings, tax bracket, quantity, total, social security and more.
Automatic Python Regex Generator
How to use the Python Regex Generator with the Konfuzio SDK:
- Import the Konfuzio SDK package and retrieve the project:
from konfuzio_sdk.data import Project
my_project = Project(id_=YOUR_PROJECT_ID)
- Get the category in which you want to train the label:
category = my_project.get_category_by_id(id_=YOUR_CATEGORY_ID)
- Create a RegexTokenizer with the appropriate rules for the label:
from konfuzio_sdk.tokenizer.regex import RegexTokenizer
label = my_project.get_label_by_name("wage type")
for regex in label.find_regex(category=category):
regex_tokenizer = RegexTokenizer(regex=regex)
- Create a ListTokenizer to group all RegexTokenizer objects together:
from konfuzio_sdk.tokenizer.base import ListTokenizer
tokenizer = ListTokenizer(tokenizers=[regex_tokenizer])
- Use the tokenizer to create an annotation for each matching element in a document:
document = my_project.get_document_by_id(YOUR_DOCUMENT_ID)
tokenizer.tokenize(document)
By training a custom regex tokenizer, organizations can customize the Python Regex Generator to their specific business context and increase the effectiveness of their document processing. Try it for yourself and learn how easy it is to define custom regex expressions and create training data.
Low Code and No-Code Regex Generator
The Konfuzio SDK Regex Generator is now available on the Konfuzio server! This means that you can now create regex rules without writing a single line of code. This feature is especially useful for those developing low-code or no-code applications.

The Konfuzio platform gives you the power to automatically structure and analyze complex text documents to extract valuable information. With the Konfuzio SDK Regex Generator, you can now also create your own rules for extracting information from unstructured text documents without having to deal with the complexity of regular expressions.
Application example

To use the custom regex generator, all you need to do is define the rules you want on the Konfuzio server and then apply them to the text documents. The Konfuzio server then uses these rules to extract and structure relevant information from your texts.
This approach allows you to quickly and easily process a wide range of text documents without having to perform complex coding or manual work processes. What's more, you can adjust and optimize the rules for automatically extracting information from your text documents at any time to continuously improve results.

The Konfuzio SDK Regex Generator is another step towards automated text analysis, allowing users to extract complex information quickly and easily. The combination of AI technologies and user-defined rules greatly facilitates and accelerates the analysis of text documents.
With Konfuzio SDK Regex Generator you can now also create your own regex rules without having to deal with the complexity of regular expressions. This is great news for anyone developing low-code or no-code applications, as it makes the job much easier and faster. Try it out and see how easy it can be to extract information from unstructured text documents!
Regex use cases
Regex (Regular Expression) is often used in word processing to identify text patterns and extract information from unstructured data sources. Here are five use cases for regex:
Use cases for regex | Description |
---|---|
Validate email addresses | Regex can be used to filter out a correct email address from a text or to detect and mark an incorrect email address. |
Identify phone numbers | Regex can be used to find and extract phone numbers in a text, for example to build a contact directory. |
Recognize dates | Regex can be used to filter out dates from a text and put them into a structured format, for example for analyzing financial reports. |
Mark keywords | Regex can be used to find and highlight specific keywords or phrases in a text, for example, to identify trends in social media posts. |
Replace words or phrases | Regex can be used to replace words or phrases in a text, for example to censor inappropriate content in an online forum. |
Regex vs. Named Entity Recognition
Although Regex can be an effective way to identify text patterns and extract information from unstructured data sources, it also has some disadvantages compared to NER (Named Entity Recognition):
Benefits | Disadvantages |
---|---|
Regex is easy to implement and can deliver results quickly | Regex can only take limited context information into account and is prone to errors when identifying text patterns |
Regex can be used to process large amounts of data and is scalable | Regex requires manual adjustment and monitoring when identifying text patterns |
Regex can also be used in unstructured text | Regex is unable to identify complex text patterns and is limited in its ability to understand semantic relationships between words |
Regex is often faster and more efficient than NER for simple text patterns | Regex is not able to recognize synonyms or variations of text patterns |
Regex can also be used in older systems or environments that may not have NER functionality | Regex requires a deep understanding of text processing and can be difficult to implement by non-experts |
Although NER is generally more powerful and versatile than Regex, Regex can still be effective in certain use cases. The choice between Regex and NER depends on the specific requirements of the use case and the available resources.
Conclusion
Python Regex Generator is a valuable tool that helps companies digitize and evaluate their documents more effectively.
With the Konfuzio SDK, Python developers can create custom labels and use the Python Regex Generator to automatically read relevant information. By continuously training and optimizing the label set, companies can keep the quality of their results at a consistently high level.
Try Konfuzio and learn how Python Regex Generator can help you digitize and evaluate your documents more effectively.
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