Duplicate Sentences Finder

The tool uses smart sentence splitting that recognizes standard sentence-ending punctuation: periods (.), exclamation marks (!), and question marks (?). It splits text at these boundaries and treats each segment as a distinct sentence. The algorithm handles edge cases like abbreviations and maintains accuracy even with inconsistent spacing. After splitting, each sentence is analyzed for duplicates based on your selected options (case sensitivity, punctuation handling).

Ignore Punctuation (enabled by default) strips all punctuation marks before comparing sentences, so minor punctuation differences don't prevent duplicate detection. Examples: 'Hello world' and 'Hello, world!' are treated as duplicates (punctuation ignored). 'Call me.' and 'Call me?' are duplicates. 'Email: john@example.com' and 'Email john@example.com' are duplicates. Disable this option if punctuation variations should be treated as different sentences—useful for code, structured data, or when exact punctuation matters.

The statistics panel shows 5 key metrics: Total: Total number of sentences detected in your text. Unique: Number of unique sentences (distinct content). Dups: Number of sentences that appear more than once (meet your min frequency). Total Dups: Total occurrence count of all duplicates. Duplication Rate: Percentage of sentences that are duplicates. Example: 100 total sentences, 85 unique, 15 appearing multiple times, 30 total duplicate occurrences = 30% duplication rate. High rates (>20%) suggest excessive repetition.

Min Frequency sets how many times a sentence must appear to be listed as a duplicate (default: 2). Set to 2 = shows sentences appearing 2+ times. Set to 3 = only shows sentences appearing 3+ times (severe repetition). Min Length filters out sentences shorter than X characters (default: 10). This ignores short phrases like 'Yes.', 'OK.', 'Thanks.', 'No problem.' that naturally repeat in documents but aren't problematic duplicates. Use Min Length: 20-30 for documents to focus on meaningful duplications, not filler responses.

Use Case Sensitive when capitalization carries meaning and should differentiate sentences. Examples: 'The President announced...' vs 'the president announced...' (title vs common noun). 'URGENT: Please respond' vs 'Urgent: Please respond' (emphasis difference). Technical documentation where 'Connect to SERVER' differs from 'Connect to server'. Leave it OFF (default) for general writing where 'Hello', 'HELLO', and 'hello' at sentence starts should be treated as identical duplicates.

Proofreading & Editing: Find accidentally copy-pasted paragraphs in essays, articles, or reports. Quality Control: Detect redundant sentences in legal documents, contracts, or technical manuals. Content Analysis: Identify repetitive messaging in marketing materials or email templates. Academic Writing: Check theses and papers for inadvertent duplication. Code Review: Find duplicate comment blocks or documentation entries. Data Validation: Spot repeated entries in logs, transcripts, or survey responses.

This tool finds exact duplicates (with optional case/punctuation normalization), not semantically similar sentences. 'The cat sat on the mat' and 'The cat sat on the mat.' are duplicates (if Ignore Punctuation is ON). 'The cat sat on the mat' and 'A cat sat on a mat' are NOT duplicates (different words). For finding semantically similar but differently worded sentences, you'd need an AI-powered paraphrase detector. This tool is perfect for catching copy-paste errors, not rewording detection.

Partially. Since the tool splits on sentence-ending punctuation (., !, ?), each sentence within a paragraph is analyzed individually. If an entire paragraph is duplicated, every sentence in that paragraph will appear as a duplicate with count ≥2. However, the tool doesn't group sentences into paragraph-level analysis. For dedicated paragraph-level duplicate detection, use the 'Duplicate Paragraphs Finder' tool. This tool is optimized for sentence-level granularity.

The current sentence splitter uses a simple period-based approach, so abbreviations with periods (Dr., Mrs., etc., i.e., e.g.) might cause incorrect sentence splits. Example: 'Dr. Smith arrived. He was late.' might split as 'Dr', 'Smith arrived', 'He was late'. To minimize issues: (1) Increase Min Length to filter out short false segments, (2) Manually review results for such texts, (3) Pre-process text to replace 'Dr.' with 'Doctor' before analysis. Most natural prose works fine; technical documents with many abbreviations may need preprocessing.

100% private. All sentence analysis happens entirely in your browser using JavaScript. Your text never leaves your device, isn't uploaded to servers, isn't logged, and isn't stored anywhere. Even file uploads are processed locally—no network transmission. Check your browser's Network tab to verify zero data sent. Essential for processing confidential documents like contracts, legal briefs, academic papers before publication, proprietary reports, or any sensitive writing requiring complete privacy and security.