I was developing a theoretical understanding of computer vision algorithms, from basic to advanced, in order to grasp the mathematics behind them and the result?
I developed A browser-based document scanner that:
- Auto-detects document boundaries
- Corrects perspective distortion
- Enhances image quality
- Processes images in 2-3 seconds
This is the workflow for the sample application:
Tech: JavaScript + Canvas API + Pure Math
No OpenCV, no ML models - just classical algorithms that have stood the test of time.
This is working sample images:
Github link:
📱 Document Scanner Pro
A browser-based document scanner built with pure Computer Vision algorithms - no external CV libraries!
✨ Features
- 📸 Camera Capture - Take photos directly from mobile/desktop camera
- 🖼️ Image Upload - Support for all image formats (JPG, PNG, WEBP, BMP, GIF, TIFF, SVG)
- 🤖 Auto Detection - Automatic document edge detection and perspective correction
- 🎨 Multiple Modes - Auto detect, Enhance, or Black & White
- ⚙️ Adjustable Settings - Custom contrast and brightness controls
- 📥 Download Results - Save processed documents as JPG
- 💻 Offline-First - Runs entirely in browser, no backend needed
- 📱 Mobile-Friendly - Responsive design for all devices
Testing demo:
🔬 Computer Vision Algorithms Implemented
This project implements 10+ classical CV algorithms from scratch without using OpenCV or any CV libraries:
Core Algorithms
| # | Algorithm | Purpose | Technique |
|---|---|---|---|
| 1 | Weighted Grayscale | Color to grayscale conversion | 0.299R + 0.587G + 0.114B |
| 2 | Histogram Stretching | Contrast |
Top comments (0)