Text Readability Analyzer
Analyze text readability with Flesch score, average sentence length, and more.
About this tool
The Text Readability Analyzer measures how easy or difficult a text is to understand by calculating key readability metrics such as Flesch Reading Ease score, word count, average sentence length, and more. Readability analysis is crucial for writers, educators, content creators, and anyone who wants to ensure their writing is accessible to their target audience. A highly technical manual requires different readability standards than a children's book or marketing copy.
Simply paste or type your text into the analyzer and it will instantly compute multiple readability scores. The tool calculates the Flesch Reading Ease score (where higher scores indicate easier reading), average words per sentence, average syllables per word, and other linguistic metrics. These metrics help you understand whether your text is at the right complexity level for your intended readers.
The tool is particularly useful for academics refining research papers, bloggers optimizing their posts for general audiences, educators preparing lesson materials, and professionals writing emails or reports. Keep in mind that readability scores are guidelines rather than rigid rules—context, tone, and formatting also play important roles in actual comprehension. Tools like this complement, not replace, careful editing and feedback from real readers.
Frequently Asked Questions
Code Implementation
import re
def text_statistics(text: str) -> dict:
"""Compute common text statistics."""
chars_with_spaces = len(text)
chars_without_spaces = len(text.replace(" ", "").replace("\t", "").replace("\n", ""))
# Words: split on whitespace, discard empty tokens
words = [w for w in re.split(r"\s+", text.strip()) if w]
word_count = len(words)
# Sentences: split on . ! ? followed by whitespace or end of string
sentences = [s for s in re.split(r"[.!?]+(?:\s|$)", text.strip()) if s.strip()]
sentence_count = len(sentences) if sentences else 0
# Paragraphs: blocks separated by one or more blank lines
paragraphs = [p for p in re.split(r"\n{2,}", text.strip()) if p.strip()]
paragraph_count = len(paragraphs) if paragraphs else 0
lines = text.splitlines()
line_count = len(lines)
avg_word_length = (
sum(len(re.sub(r"[^\w]", "", w)) for w in words) / word_count
if word_count else 0
)
# Reading time: ~238 wpm for adults (2023 meta-analysis)
reading_time_sec = round(word_count / 238 * 60)
return {
"chars_with_spaces": chars_with_spaces,
"chars_without_spaces": chars_without_spaces,
"words": word_count,
"sentences": sentence_count,
"paragraphs": paragraph_count,
"lines": line_count,
"avg_word_length": round(avg_word_length, 2),
"reading_time_sec": reading_time_sec,
}
sample = """The quick brown fox jumps over the lazy dog.
Pack my box with five dozen liquor jugs!
How vexingly quick daft zebras jump?
"""
stats = text_statistics(sample)
for key, value in stats.items():
print(f"{key:25}: {value}")Comments & Feedback
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