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Calculate the Index of Coincidence (IC) for cryptanalysis and language identification with English letter frequency statistics.
Please enter text with at least 2 letters to calculate the Index of Coincidence.
The Index of Coincidence (IC) is a statistical measure used to quantify the uniformity of letter distribution in a text. It is particularly useful in cryptanalysis to help determine if a text is encrypted and what encryption method might have been used.
An IC value close to 0.0667 suggests the text is likely a natural language (e.g., English); an IC value close to 0.0385 suggests the text is random or well-encrypted. Normalized IC multiplies the result by 26 (the number of English letters) to make it easier to interpret.
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When you need to determine if an encrypted text retains natural language characteristics, the Index of Coincidence (IC) is a crucial metric. By analyzing English letter frequencies, this tool calculates the probability of two randomly selected letters being identical. It outputs an IC value typically ranging from 0.0385 (completely random text) to 0.067 (standard English), providing a quantitative basis for cryptanalysis.
What does an IC value of 0.05 mean?
It indicates that the text's randomness falls between natural English (0.067) and complete randomness (0.0385), suggesting it may have undergone weak encryption.
Why is a minimum of 100 characters required?
Short texts are prone to significant statistical fluctuations. A minimum of 100 characters ensures the IC value is representative, though 300+ characters are recommended for higher accuracy.
The analysis results only reflect letter distribution characteristics and do not involve semantics. For texts exceeding 100,000 characters, we recommend processing them in segments. All calculations are performed locally in your browser, ensuring there is no risk of data leaks or uploads.
The typical IC value for English text is approximately 0.067, French is 0.078, and German is 0.076. When analyzing a Vigenère cipher, you can divide the ciphertext into segments and calculate the IC for each. If the IC values of the segments at a specific length closely match the English baseline, that length is highly likely to be the key length.