https://github.com/luis-varona/shadowseek
A CLI tool for near-duplicate detection in text files, written in Rust with no dependencies on runtime environments.
https://github.com/luis-varona/shadowseek
duplicate-detection minhash near-duplicate-detection simhash text-classification
Last synced: 11 months ago
JSON representation
A CLI tool for near-duplicate detection in text files, written in Rust with no dependencies on runtime environments.
- Host: GitHub
- URL: https://github.com/luis-varona/shadowseek
- Owner: Luis-Varona
- License: apache-2.0
- Created: 2025-05-04T06:45:01.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-11T00:17:18.000Z (about 1 year ago)
- Last Synced: 2025-05-16T14:13:13.073Z (about 1 year ago)
- Topics: duplicate-detection, minhash, near-duplicate-detection, simhash, text-classification
- Language: Rust
- Homepage:
- Size: 30.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE-APACHE
Awesome Lists containing this project
README
# shadowseek


[](https://github.com/Luis-Varona/shadowseek/actions/workflows/rust.yml?query=branch%3Amain)
*ShadowSeek* is a CLI tool for near-duplicate detection in text files. Written in native Rust, it offers fast execution and low memory overhead, with no dependencies on external runtime environments. Using the [`textract`](https://crates.io/crates/textract), [`rtf-parser`](https://crates.io/crates/rtf-parser), and [`epub`](https://crates.io/crates/epub) crates to parse various text file formats, it uses SimHash to quickly filter out highly dissimilar documents then applies a more sophisticated MinHash algorithm to identify near-duplicates with high accuracy.
Inspired by [Dr. Paweł Mandera](https://github.com/pmandera)'s near-duplicate detection tool [*Duometer*](https://github.com/pmandera/duometer), *ShadowSeek* aims to provide a more lightweight and efficient alternative. Development in Rust allows users to run a precompiled binary without needing to install a Java runtime environment; as an added bonus, this also reduces startup time and memory usage. The inclusion of SimHash as a first-pass filter also facilitates faster elimination of dissimilar documents, minimizing the number of comparisons performed in the more computationally expensive MinHash stage.
**(CURRENTLY UNDER DEVELOPMENT)**