Nâmedâr is an offline-first Optical Mark Recognition (OMR) platform designed for educators. I built it to solve a major problem in EdTech: the privacy risks and latency of uploading sensitive student exam data to cloud servers for grading.

What I Built I engineered a full-stack solution that allows teachers to create, scan, and instantly grade paper exams entirely within their web browser. To achieve this without relying on backend servers, I developed a high-performance computer vision engine using Rust and WebAssembly (WASM). This handles complex image perspective correction and grading locally on the user's device, guaranteeing a "zero-upload" privacy model where student data never leaves the computer.

Key Features & Technical Achievements:

- Advanced In-Browser Processing: Leveraged Rust and WASM to bring heavy optical scanning computations directly to the frontend, resulting in lightning-fast, offline grading.

- Modern Frontend Architecture: Built a highly responsive UI using React and TanStack Start.

- Intuitive Form Builder: Implemented a drag-and-drop interface allowing educators to design custom bubble sheets easily.

- Data Portability: Engineered multi-format data exports (CSV, JSON, XML) so teachers can seamlessly sync grades with their school's existing systems.

The Impact This project demonstrates my ability to take a complex, backend-heavy concept (computer vision/grading) and successfully migrate it to the frontend. It showcases my expertise in combining modern web frameworks (React) with systems-level languages (Rust) to build fast, secure, and privacy-first web applications.