What is the Readability Score? Meaning, Use-cases & Approaches

This blog discusses the importance of readability scores and provides insights into how they are used in various fields. It also suggests tools such as ApyHub's API for automatically calculating readability scores.
What is the Readability Score? Meaning, Use-cases & Approaches
Sohail Pathan
Last updated on November 22, 2023


Reading is like breathing - it's something most of us do regularly without thinking much about it. But just as air quality impacts how easily we can breathe, the complexity of what we're reading affects how smoothly we can absorb the information. So how can we judge whether the piece of information is easy to absorb?
This is where readability scores come in handy.

What is the Readability Score and why is it Important?

Readability scores are mathematical formulas that analyze sentences and vocabulary to quantify how easy or challenging a piece of text is to read. One of the known formulas used to assess the readability of a piece of text, whether it's a sentence or a paragraph, is the Flesch Reading Ease Score.
This score was developed by Rudolph Flesch, a reading consultant who was concerned that students were not learning to read well enough. The score is based on the average length of sentences and the average number of syllables per word in the text. Flesch believed that many of the texts that students were reading were too difficult for them to understand.
The Flesch Reading Ease score is calculated using a formula that takes into account the average number of words per sentence and the average number of syllables per word. A score below 30 is very difficult to read, while a score in the range of 60 to 70 is fairly easy to read.
In short - the higher the reading score, the easier a piece of text is to read.
If you’re curious, here is some more info about the foundation, grading level, and how it’s calculated.

Some common use cases of the Readability Score

Now that we have established what exactly the readability score is, let's now explore the use cases and how it can be beneficial for different audiences


The Flesch Reading Ease score can help instructional designers, teachers, and other educators match the reading materials to their students' reading abilities. For example, an LMS app has a Flesh Reading Score analyzing feature that can help teachers draft the tutorials and notes as per the understanding level of students. This can help them select appropriate resources for their students based on their reading abilities or grade levels.

Content Generation

Content creators, like writers, journalists, and bloggers, can use the Flesch Reading Ease score to make sure their content is easy to read and accessible to their target audience. By aiming for a specific score range, they can adjust their writing style and word choices accordingly. For example, a blogging platform can offer a feature to writers to check the readability score before publishing a blog post, to ensure it reaches the intended audience and gets more engagement.


The Flesch Reading Score can also help in creating legal documents. For example, A tech startup is preparing the terms and conditions for their new app. Using the Flesh Reading Score tool, they can ensure users can understand the legal terms, rather than being overwhelmed by the complex language.


The Flesch Reading Ease score can be used as a guideline for creating accessible content for individuals with different reading abilities. For example, the SaaS company could add readability scoring to their content editing workflows where the content is focused on onboarding, pricing pages, or user guides.
Now that we have covered some of the use cases of the Readability Score and its ability, let's now explore the possible approaches and resources available for calculating the Readability Score. ‘
In general, these approaches are:
  • Using Visual Tools
  • Using Open Source Libraries
  • Using Cloud APIs.
There is no right or wrong approach, it strongly depends on the use case, available resources, availability of developers, etc. Each of the approaches comes with its own benefits and we will see some of them below.

Visual Tools

  1. Readable: Readable is the most known tool when it is readable. This lets you check the readability of text, documents, and websites. It is not just limited to readability but also grammar checks, sentiment analysis, and advanced factors like keyword density and text statistics.
  1. Semrush SEO Assistant: Semrush SEO Assistant is a Chrome extension that primarily creates SEO-optimized content. Using the Flesch-Kincaid score to assess and improve your text’s readability, it offers recommended keywords, tone of voice, and plagiarism checks.
  2. Originality.ai: Originality utilizes AI in addition to the Flesch Kincaid Ease Score checker. It also offers plagiarism detection, fact-checking, and fact-checking services.
These tools are suitable for non-developers like content creators who can identify and evaluate the content for specific audiences. The only challenge is manual effort if you try to extract data from thousands of pages.

Open Source Libraries / Frameworks:

Popular open-source libraries like:
  1. Whelk-i: This is a Java library that is used to perform Flesch-Kincaid readability tests. It has easy-to-understand documentation.
  2. Py-readability-metrics:py-readability-metrics is an open-source library available in Python that D is actively maintained by the community. One of the advantages is this lets you check the readability not only for Flesch-Kincaid but with various algorithms and formulas like Gunning Fog, ARI, Dale Chall, SMOG, Space, and more.
Libraries are a programmatic way that could be suited for medium-scale projects, but they have some limitations depending on the tech stack. For example, there may be fewer robust libraries available for other programming languages. Libraries also need to be updated by their maintainers to stay compatible with new language versions and fixes.
This is where cloud APIs can help bridge the gap. Rather than relying solely on libraries, cloud APIs allow you to access powerful pre-built services over the network through simple API calls.

Cloud APIs:

  1. Sapling: Saplings are offered by naming them as Statistic API. For developers, with simple documentation, it also offers language-specific client code in JavaScript and Python.
  2. SEOReviewTools: SEOReviewTools offers a service that quickly calculates the Flesch-Kincaid readability score for any URL. For developers, with simple documentation, it also offers language-specific client code in Python and PHP
  3. ApyHub: ApyHub's Readability Score & Readability Score Documents is a cloud-based API that enables you to check the readability of texts and documents seamlessly. Users can simply pass the text, PDF, or Docs and get back a response that includes the overall score, grade level, class of the reader, and more. It offers up to 2 million API calls on a free plan. You can even use the visual API Playground to test the output beforehand.     apyhub

Conclusion :

Ultimately, the Flesch Reading Ease score can assist with creating content that is easy to read and understand, which is important for anyone who wants to communicate their message to their audience effectively. The approach depends on the use case and requirement.
In order to calculate the Readability Score, there are visual tools available. On a programmatic level, APIs like the ApyHub API can help developers incorporate the Readability Scores calculation inside their application and automate the process.
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