Implement OCR Engine

[mftee]

Create high-performance OCR library with Tesseract bindings

Description:
Build a Rust-based OCR engine that processes images and extracts text with high accuracy.

Requirements:

• Tesseract bindings for Rust
• Image preprocessing (grayscale, noise reduction, deskew)
• Multi-language support
• Batch processing capability
• Confidence scores for extracted text
• Bounding box detection
• CLI tool for standalone usage
• Node.js FFI bindings
• Performance optimizations
• Thread-safe operations

Acceptance Criteria:

• OCR accuracy matches or exceeds Tesseract.js
• Preprocessing improves accuracy
• Processes images 2x faster than JS alternatives
• CLI tool processes batches
• FFI bindings work correctly
• Comprehensive tests with test images
• 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 full-stack and blockchain developer with strong experience in Rust, Python, and TypeScript, and a solid background in machine learning and statistical analysis. I’ve reviewed the requirements for #141 – Implement OCR Engine, and I’d like to take this on, as it aligns closely with my experience building high-performance Rust systems and image/text processing pipelines. My approach is to implement a Rust-based OCR engine using Tesseract with proper preprocessing (grayscale, noise reduction, and OpenCV deskewing), thread-safe batch processing, a CLI for standalone usage, and Node.js FFI bindings to ensure accuracy, performance, and clean integration with the rest of the project.

ℹ️ Repo Maintainers: To accept this application, review their application or assign @ChukwuemekaP1 to this issue.

[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 full-stack and blockchain developer with strong experience in Rust, Python, and TypeScript, and a solid background in machine learning and statistical analysis. I’ve reviewed the requirements for #141 – Implement OCR Engine, and I’d like to take this on, as it aligns closely with my experience building high-performance Rust systems and image/text processing pipelines. My approach is to implement a Rust-based OCR engine using Tesseract with proper preprocessing (grayscale, noise reduction, and OpenCV deskewing), thread-safe batch processing, a CLI for standalone usage, and Node.js FFI bindings to ensure accuracy, performance, and clean integration with the rest of the project.

ℹ️ Repo Maintainers: To accept this application, review their application or assign @ChukwuemekaP1 to this issue.

[ShantelPeters]

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

I’m eager to work on the high-performance OCR library because it combines my Rust expertise with real-world image processing challenges. I have experience building Rust-native libraries with safe, thread-aware FFI bindings and optimizing performance-critical operations. Leveraging Tesseract bindings, I can implement preprocessing pipelines—grayscale conversion, noise reduction, and deskewing—to maximize OCR accuracy, while supporting multi-language text and batch processing. I’m confident in delivering a CLI tool, Node.js bindings, and a library that is both faster and more accurate than JS alternatives, with robust tests, confidence scores, and clear documentation.

ℹ️ Repo Maintainers: To accept this application, review their application or assign @ShantelPeters to this issue.

[ryzen-xp]

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

Hi maintainers 👋
I’m a blockchain developer with strong expertise in Stellar, Rust,TS and Stellar SDK integrations. I’ve worked on multiple Stellar-based smart contract and backend systems.

I understand the scope of this issue and can deliver a reliable, production-quality solution with proper tests and documentation.
Plz assign me !!

ETA: 24 hours

ℹ️ Repo Maintainers: To accept this application, review their application or assign @ryzen-xp to this issue.