Persian Math OCR - Automated Math Content Extractor (AMCE)

استخراج هوشمند محتوای ریاضی فارسی از فایل‌های PDF

🎯 What is AMCE?

AMCE (Automated Math Content Extractor) is an intelligent web application that automatically extracts Persian text and mathematical formulas from PDF documents using AI-powered vision models. It converts PDFs into structured, editable content with perfect LaTeX rendering.

Core Workflow: PDF → Image → LaTeX

The backbone of this project follows a sophisticated pipeline:

📄 PDF Document
    ↓
🖼️  Image Conversion (pdf-to-img)
    ↓
🤖 AI Vision Processing (OpenRouter/GPT-4o/Claude)
    ↓
📝 Structured JSON Output (Persian Text + LaTeX)
    ↓
💾 MongoDB Storage
    ↓
✨ Beautiful RTL Rendering (React + KaTeX)

Why This Approach?

PDF → Image: Converts each PDF page into high-resolution images (2x scale) to preserve visual context, mathematical notation, and Persian typography
Image → AI Processing: Vision models (GPT-4o/Claude) analyze images with superior accuracy for:
- Persian/Arabic text recognition
- Mathematical formula detection
- Layout understanding
- Mixed content (text + math) parsing
AI → LaTeX: Extracts content into structured JSON with:
- Double-escaped LaTeX (\\frac for proper JSON encoding)
- Markdown formatting preservation
- Multiple-choice question parsing
- Difficulty estimation

✨ Key Features

🎨 Intelligent Extraction

Persian/Farsi Support: Full RTL text rendering with Vazirmatn font
LaTeX Math Rendering: Perfect formula display using KaTeX
Mixed Content: Seamlessly handles Persian prose with embedded math formulas
High Accuracy: >95% transcription accuracy for both text and formulas

📊 Structured Data Output

Multiple-Choice Parsing: Automatically separates question stems from answer options
JSON Schema: Validated output using Zod for type safety
MongoDB Storage: Persistent, searchable database of extracted content

🖥️ Modern UI/UX

RTL Layout: Native right-to-left support for Persian content
Live Editing: Inline editing of extracted problems and options
Beautiful Design: Modern gradient-based UI with glassmorphism effects
Responsive: Works perfectly on desktop and mobile devices

🔧 Developer-Friendly

Type-Safe: Full TypeScript implementation
API Routes: RESTful API for document management
Error Handling: Graceful fallbacks and user-friendly error messages

🚀 Quick Start

Prerequisites

Node.js 20+ or 22+
MongoDB Atlas account (or local MongoDB)
OpenRouter API key (Get one here)

Installation

Clone the repository

git clone https://github.com/yourusername/persian-math-ocr.git
cd persian-math-ocr

Install dependencies
```
npm install
```
Configure environment variables

Create .env.local file:
```
MONGODB_URI=mongodb+srv://username:password@cluster.mongodb.net/database
OPENROUTER_API_KEY=sk-or-v1-your-api-key-here
OPENROUTER_MODEL=openai/gpt-4o
NEXT_PUBLIC_APP_URL=http://localhost:3000
```
Recommended Models:
- openai/gpt-4o - Best for vision/OCR accuracy
- anthropic/claude-3.5-sonnet - Excellent for structured output
- google/gemini-pro-1.5 - Good balance of speed and accuracy
Run development server
```
npm run dev
```
Open your browser

Navigate to http://localhost:3000

📖 How It Works

Step-by-Step Process

Upload PDF: Drag and drop or select a PDF file containing Persian math problems
Image Conversion: Each page is converted to a high-resolution JPEG image (2x scale)
AI Processing: Images are sent to OpenRouter's vision API with a specialized prompt
Extraction: The AI extracts:
- Document title
- Problem statements (Persian text + LaTeX formulas)
- Multiple-choice options (if present)
- Difficulty estimation
Validation: Output is validated using Zod schema
Storage: Structured data is saved to MongoDB
Rendering: Beautiful RTL interface displays content with proper math rendering

Example Output Structure

{
  "document_title": "بانک تست فیزیک ۱۰",
  "problems": [
    {
      "id": 1,
      "content": "درون کره‌ای آهنی به چگالی $\\frac{8}{cm^3}$ حفره‌ای به حجم $2000cm^3$ وجود دارد...",
      "options": [
        { "label": "A", "text": "پاسخ اول" },
        { "label": "B", "text": "پاسخ دوم" }
      ],
      "difficulty_estimation": "Medium"
    }
  ]
}

🛠️ Tech Stack

Category	Technology
Framework	Next.js 15 (App Router)
Language	TypeScript
Database	MongoDB + Mongoose
Styling	Tailwind CSS v4
AI/OCR	OpenRouter API (GPT-4o, Claude, Gemini)
PDF Processing	pdf-to-img
Math Rendering	KaTeX + react-markdown + rehype-katex
Validation	Zod
UI Components	Lucide React Icons
Notifications	Sonner

📁 Project Structure

persian-math-ocr/
├── src/
│   ├── app/
│   │   ├── api/
│   │   │   ├── upload/          # PDF upload endpoint
│   │   │   └── documents/       # Document CRUD endpoints
│   │   ├── documents/[id]/      # Document detail view
│   │   ├── layout.tsx           # Root layout with RTL support
│   │   ├── page.tsx             # Dashboard
│   │   └── globals.css          # Tailwind v4 styles
│   ├── components/
│   │   ├── FileUpload.tsx       # Drag-and-drop upload
│   │   └── ProblemCard.tsx      # Problem display & editing
│   ├── lib/
│   │   ├── pdf-processor.ts     # PDF → Image conversion
│   │   ├── llm.ts              # OpenRouter AI integration
│   │   ├── validation.ts       # Zod schemas
│   │   └── db.ts               # MongoDB connection
│   ├── models/
│   │   └── Document.ts         # Mongoose schema
│   └── types/
│       └── index.ts            # TypeScript types
├── public/                      # Static assets
├── .env.local                  # Environment variables (not in git)
└── README.md                   # This file

🎨 Features in Detail

Persian Text Support

RTL Layout: Automatic right-to-left text direction
Vazirmatn Font: Beautiful Persian typography
UTF-8 Encoding: Perfect character preservation
BiDi Handling: Math formulas remain LTR within RTL text

LaTeX Math Rendering

Inline Math: $x^2 + y^2 = z^2$
Block Math: $$\int_0^\infty e^{-x^2} dx = \frac{\sqrt{\pi}}{2}$$
Double Escaping: Automatic \\frac handling for JSON
Error Boundaries: Graceful fallback for malformed LaTeX

Editing Interface

Inline Editing: Click edit button to modify content
Live Preview: See changes in real-time
Option Management: Add/edit/remove multiple-choice options
Save to Database: Persistent storage of edits

🔧 API Endpoints

`POST /api/upload`

Upload and process a PDF file.

Request:

Content-Type: multipart/form-data
file: PDF file

Response:

{
  "_id": "...",
  "document_title": "...",
  "problems": [...],
  "createdAt": "...",
  "updatedAt": "..."
}

`GET /api/documents`

List all processed documents.

`GET /api/documents/[id]`

Get a specific document with all problems.

`PUT /api/documents/[id]`

Update document (e.g., edited problems).

🎯 Use Cases

Educational Institutions: Convert Persian math textbooks to digital format
Content Management: Build searchable question banks
E-Learning Platforms: Import problems from PDF worksheets
Research: Extract mathematical content for analysis
Publishers: Digitize legacy Persian math publications

📝 Environment Variables

Variable	Description	Required
`MONGODB_URI`	MongoDB connection string	✅ Yes
`OPENROUTER_API_KEY`	OpenRouter API key	✅ Yes
`OPENROUTER_MODEL`	Model to use (default: `openai/gpt-4o`)	❌ No
`NEXT_PUBLIC_APP_URL`	App URL for OpenRouter headers	❌ No

🐛 Troubleshooting

Styles Not Loading

If Tailwind styles aren't appearing, ensure you're using Tailwind CSS v4 syntax:

@import "tailwindcss";

PDF Processing Errors

Ensure pdf-to-img dependencies are installed
Check that PDF files are not corrupted
Verify OpenRouter API key is valid

MongoDB Connection Issues

Check your connection string format
Ensure IP whitelist includes your server IP
Verify database credentials

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Fork the repository
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

OpenRouter for providing access to multiple AI models
Mozilla PDF.js for PDF processing capabilities
KaTeX for beautiful math rendering
Next.js team for the amazing framework

📧 Contact

For questions, issues, or suggestions, please open an issue on GitHub.

Made with ❤️ for the Persian-speaking educational community

⭐ Star this repo if you find it useful!

# persian-math-ocr

Name		Name	Last commit message	Last commit date
Latest commit History 14 Commits
.github/ISSUE_TEMPLATE		.github/ISSUE_TEMPLATE
public		public
src		src
.gitignore		.gitignore
CODEBASE_STRUCTURE.md		CODEBASE_STRUCTURE.md
README.md		README.md
eslint.config.mjs		eslint.config.mjs
next.config.ts		next.config.ts
package-lock.json		package-lock.json
package.json		package.json
postcss.config.mjs		postcss.config.mjs
tsconfig.json		tsconfig.json

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Persian Math OCR - Automated Math Content Extractor (AMCE)

🎯 What is AMCE?

Core Workflow: PDF → Image → LaTeX

Why This Approach?

✨ Key Features

🎨 Intelligent Extraction

📊 Structured Data Output

🖥️ Modern UI/UX

🔧 Developer-Friendly

🚀 Quick Start

Prerequisites

Installation

📖 How It Works

Step-by-Step Process

Example Output Structure

🛠️ Tech Stack

📁 Project Structure

🎨 Features in Detail

Persian Text Support

LaTeX Math Rendering

Editing Interface

🔧 API Endpoints

POST /api/upload

GET /api/documents

GET /api/documents/[id]

PUT /api/documents/[id]

🎯 Use Cases

📝 Environment Variables

🐛 Troubleshooting

Styles Not Loading

PDF Processing Errors

MongoDB Connection Issues

🤝 Contributing

📄 License

🙏 Acknowledgments

📧 Contact

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`PUT /api/documents/[id]`

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