Implement Document Similarity Analyzer

[mftee]

Create ML-based document similarity detection system in Rust

Description:
Build a Rust library that compares documents and detects potential duplicates or forgeries using ML techniques.

Requirements:

• Text similarity algorithms (cosine similarity, Levenshtein)
• Image similarity using perceptual hashing
• Feature extraction for comparison
• Clustering similar documents
• Anomaly detection
• Batch comparison operations
• CLI tool for comparison
• Node.js FFI bindings
• Performance optimizations (SIMD)
• Configurable similarity thresholds

Acceptance Criteria:

• Similarity scores are accurate
• Detects duplicate documents reliably
• Image comparison works for scanned docs
• Batch operations are efficient
• CLI tool is functional
• FFI bindings are performant
• Unit tests with test datasets
• Documentation with examples

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[ChukwuemekaP1]

@ChukwuemekaP1 has applied to work on this issue as part of the Stellar Wave Program's 1st wave.

Hi, my name is Paul. I’m a Fullstack developer with experience in Rust, Python, and TypeScript, and I’ve built ML-based systems for text and image processing before.

Here’s how I plan to implement the Document Similarity Analyzer:

Extract text and images from documents, normalize them, and create vector representations (TF-IDF for text, perceptual hashes for images).

Compute similarity using cosine similarity for text and hash comparisons for images.

Support batch comparisons, clustering of similar documents, and anomaly detection for potential forgeries.

Provide a CLI tool and Node.js bindings for easy integration.

Optimize performance using SIMD and parallel processing.

Include unit tests, example datasets, and clear documentation to ensure reliability and usability.

I’m confident this flow will produce an accurate, efficient, and user-friendly solution, and I’m ready to get started.

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