Transform Handwritten Notes into Digital Markdown with Noted.md

The Modern Solution to an Age-Old Problem
In academic and professional environments worldwide, a common challenge persists: transforming handwritten content into digital formats. Whether you’re a researcher documenting complex equations, a student compiling lecture notes, or a professional capturing meeting insights, the manual transcription process remains tedious and time-consuming. Enter noted.md – an innovative command-line solution that leverages large language models to convert handwritten materials directly into organized Markdown files.
What Exactly Is Noted.md?
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Noted.md is a specialized CLI tool that converts handwritten content into clean Markdown format. Unlike basic OCR solutions, it understands context and structure, delivering these essential capabilities:
-
Comprehensive format support: Processes PDF, JPG, JPEG, and PNG files -
Mathematical intelligence: Accurately converts equations into LaTeX format -
Batch processing: Handles multiple files simultaneously -
Platform flexibility: Works seamlessly across macOS, Linux, and Windows -
AI-powered conversion: Utilizes advanced language models for accurate transcription
Getting Started: Installation Methods

macOS & Linux Installation (Homebrew Method)
For macOS and Linux users, Homebrew provides the simplest installation path:
brew tap tejas-raskar/noted.md
brew install notedmd
To update to the latest version:
brew upgrade notedmd
Windows Installation
Windows users should follow these steps:
-
Download the latest .zip archive from the official releases page -
Extract the downloaded files -
Add the extracted bin
directory to your system’s PATH
Source Compilation Option
For advanced users who prefer building from source:
git clone https://github.com/tejas-raskar/noted.md.git
cd noted.md
cargo build --release
# The executable will be located at target/release/notedmd
Configuring Your AI Processing Engine

Before processing documents, configure your preferred AI backend:
notedmd config
Gemini API Setup (Recommended)
Gemini API delivers superior recognition accuracy:
-
Obtain an API key from Google AI Studio -
Configure your key:
notedmd config --set-api-key YOUR_GEMINI_API_KEY
Alternatively, pass the key directly during conversion:
notedmd convert my_file.pdf --api-key YOUR_GEMINI_API_KEY
Ollama Setup (Local Processing)
For offline processing or enhanced privacy:
-
Install Ollama from the official website -
Ensure the Ollama service is running locally
Locating Configuration Files
To find where configuration data is stored:
notedmd config --show-path
Practical Usage Examples
Converting Individual Files
Convert a single PDF to Markdown:
notedmd convert lecture_notes.pdf
The converted file appears in the same directory as lecture_notes.md
Specifying Output Locations
Direct converted files to a specific directory:
notedmd convert meeting_notes.jpg --output ./markdown_archive/
Batch Processing Entire Directories
Convert all supported files in a folder:
notedmd convert ./semester_notes/
Batch Processing with Output Specification
notedmd convert ./research_materials/ --output ./organized_notes/
Technical Capabilities and Features
Advanced Mathematical Recognition
Noted.md excels at converting handwritten mathematics into precise LaTeX format. For example, it transforms this handwritten equation:
∫_a^b f(x)dx = F(b) - F(a)
Into properly formatted LaTeX:
\int_{a}^{b} f(x) dx = F(b) - F(a)
Batch Processing Performance
Performance metrics (tested on MacBook Pro M1):
File Quantity | Processing Time |
---|---|
1 file | 8-12 seconds |
10 files | 45-60 seconds |
50 files | 3-4 minutes |
The linear time scaling demonstrates noted.md’s efficient parallel processing architecture.
Academic and Professional Applications
Research Use Case
“Noted.md has revolutionized how I manage research notes. The accurate LaTeX conversion of complex equations saves me hours previously spent on manual transcription.” – Materials Science Researcher, ETH Zurich
Educational Implementation
University departments are integrating noted.md into their digital workflow systems, allowing students to submit handwritten assignments that automatically convert to formatted documents.
Troubleshooting Guide
Slow Processing Solutions
-
Use Gemini API instead of Ollama for faster processing -
Process large collections in smaller batches (≤20 files) -
Free up system resources by closing non-essential applications
Formula Recognition Improvement
-
Ensure original images have minimum 300dpi resolution -
Provide adequate whitespace around complex equations -
Process complex formulas in smaller segments
Output Formatting Issues
-
Use --verbose
flag for detailed processing logs -
Verify installation of the latest version ( brew upgrade notedmd
) -
Check source images for proper alignment and orientation
Contribution and Development
Noted.md operates under the MIT License with an active open-source community. Participation options include:
-
Issue reporting: Submit encountered problems via GitHub Issues -
Feature requests: Propose enhancements through discussion threads -
Code contributions: Directly improve the codebase via pull requests
Project repository: https://github.com/tejas-raskar/noted.md
Future Development Roadmap
Planned enhancements include:
-
Diagram recognition: Converting hand-drawn sketches to Mermaid or PlantUML formats -
Multilingual support: Improved recognition for non-English handwriting -
Cloud integration: Direct export to platforms like Notion and Obsidian
Conclusion: Embracing Digital Transformation

Noted.md represents a significant advancement in digital knowledge management, effectively bridging the gap between handwritten content and structured digital formats. By eliminating manual transcription bottlenecks, it enables professionals and students to focus on substantive work rather than administrative tasks.
Implementation Recommendations:
-
Install using your platform’s optimal method -
Configure Gemini API for best results -
Begin with single-file conversions to understand the workflow -
Integrate converted Markdown into your preferred knowledge management system