Skip to main content
I
Uni
UNICODE
Tools/Frequency Analyzer

Unicode Character Frequency & N-Gram Studio

Analyze character frequency distributions, bigram/trigram N-grams, script percentage bars, and Shannon information entropy in real time.

161Total Characters
46Unique Glyphs
4.793 bitsShannon Information Entropy
3 ScriptsDetected Script Families
Quick Presets:
Font Size (16px):
Input Text BufferLive Character Analysis Stream

Multilingual Script Breakdown

Basic Latin (ASCII)116 chars (72%)
Other / Symbol44 chars (27.3%)
Emojis & Symbols1 chars (0.6%)
Ranked by Occurrence Density
RankTokenOccurrencesPercentage DensityUnicode CodepointScript Family
#1␣ (Space)21
13.04%
U+0020Other / Symbol
#2r12
7.45%
U+0072Basic Latin (ASCII)
#3s12
7.45%
U+0073Basic Latin (ASCII)
#4i11
6.83%
U+0069Basic Latin (ASCII)
#5e10
6.21%
U+0065Basic Latin (ASCII)
#6c8
4.97%
U+0063Basic Latin (ASCII)
#7a8
4.97%
U+0061Basic Latin (ASCII)
#8n7
4.35%
U+006EBasic Latin (ASCII)
#9t6
3.73%
U+0074Basic Latin (ASCII)
#10,6
3.73%
U+002COther / Symbol
#11o5
3.11%
U+006FBasic Latin (ASCII)
#12d5
3.11%
U+0064Basic Latin (ASCII)
#13l5
3.11%
U+006CBasic Latin (ASCII)
#1403
1.86%
U+0030Other / Symbol
#15w3
1.86%
U+0077Basic Latin (ASCII)
#1612
1.24%
U+0031Other / Symbol
#17p2
1.24%
U+0070Basic Latin (ASCII)
#1892
1.24%
U+0039Other / Symbol
#19b2
1.24%
U+0062Basic Latin (ASCII)
#20k2
1.24%
U+006BBasic Latin (ASCII)
#21y2
1.24%
U+0079Basic Latin (ASCII)
#22m2
1.24%
U+006DBasic Latin (ASCII)
#23C2
1.24%
U+0043Basic Latin (ASCII)
#24U1
0.62%
U+0055Basic Latin (ASCII)
#2561
0.62%
U+0036Other / Symbol
#26.1
0.62%
U+002EOther / Symbol
#2751
0.62%
U+0035Other / Symbol
#2841
0.62%
U+0034Other / Symbol
#2981
0.62%
U+0038Other / Symbol
#30u1
0.62%
U+0075Basic Latin (ASCII)

The Science of Text Frequency Analysis & Shannon Information Entropy

Character frequency analysis is one of the oldest and most fundamental disciplines in linguistics, computational linguistics, and classical cryptography. By analyzing the statistical occurrence of letters and N-grams across a corpus, computer scientists can automatically identify natural human languages, train machine learning tokenizers, and detect steganographic tampering:

1. Shannon Information Entropy ($\mathcal{H}$):Formulated by Claude Shannon in 1948, entropy $\mathcal{H} = -\sum p_i \log_2(p_i)$ measures the expected information density per character. Natural human languages feature predictable letter clustering with an entropy between 3.5 and 4.8 bits/char.
2. N-Gram Language Modeling:Bigrams (2-char pairs) and trigrams (3-char triplets) capture linguistic phonetic patterns (such as ‘th’, ‘ing’ in English or ‘ال’ in Arabic).

Applications in Modern AI & Cryptanalysis

  • •LLM Byte-Pair Encoding (BPE): Subword tokenizers rely on high-frequency bigram merges to optimize vocabulary compression.
  • •Cipher Breaking: Substitution ciphers (like Caesar or ROT-13) preserve letter frequencies, allowing automated decryption.
  • •Steganography & Bot Detection: Anomaly spikes in rare zero-width characters cause sudden entropy shifts detectable by this tool.
  • •100% In-Browser Privacy: Analyze sensitive documents with zero remote server data transmission.

Frequently Asked Questions (FAQs)

How is Shannon Entropy calculated for text?+

Shannon Entropy calculates the average information content using the formula H = -sum(p * log2(p)), where p is the probability of each unique character. A lower entropy indicates repetitive text, while a higher entropy indicates high randomness.

Why are spaces and newlines visible in the frequency breakdown?+

Whitespace characters (represented as ␣ and ↵) are fundamental structural components of text that heavily influence word boundary segmentation and compression algorithms.

Can I upload large text documents or books for analysis?+

Yes! Click "Upload .TXT" to load long text files. The tool processes tens of thousands of words instantly in client-side memory.