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SketchCalc

SketchCalc is an interactive web application that allows users to draw mathematical expressions directly on a canvas. The application uses machine learning to predict the drawn symbols and converts them into a formal mathematical expression. It then evaluates the expression to provide the calculated answer.

SketchCalc Preview

Features

  • Drawing Canvas: Users can draw mathematical symbols and expressions on a canvas using mouse input.
  • Real-time Prediction: The application predicts the drawn symbols and converts them into mathematical notation.
  • Bounding Box Calculation: Drawn symbols are enclosed in bounding boxes to isolate individual symbols.
  • Integration and Differentiation: Supports integral and differential calculus with custom variables.
  • Evaluation: The predicted mathematical expressions are evaluated to provide the answer.
  • Custom Variables: Users can include custom variables in their expressions.
  • Elapsed Time Measurement: The time taken to parse and evaluate the drawing is displayed.

How It Works

  1. Drawing: Users draw mathematical expressions on the canvas.
  2. Path Storage: The paths of the drawings are stored and processed to determine bounding boxes.
  3. Prediction: Each bounding box is sent to a server that uses a machine learning model to predict the symbol.
  4. Display: The predicted symbols are displayed on the canvas.
  5. Calculation: The final expression is evaluated, and the result is displayed to the user.

Example Usage

  • Draw an integral on the canvas, and the application will recognize it as an integral symbol, convert it to the proper mathematical notation, and calculate the result.
  • Use custom variables in your expressions to evaluate them dynamically.

Technology Stack

  • Frontend: JavaScript, HTML5 Canvas, CSS
  • Backend: Flask (for prediction API)
  • Machine Learning: TensorFlow

Installation

  1. Clone the repository: 
    git clone https://github.com/Frost-Lord/SketchCalc.git
cd SketchCalc
  2. Download the model and dataset 
    https://drive.google.com/drive/folders/1u9L_ByfnE8vfx2AMju7VChWGTsQkbbgm?usp=sharing
Extract "model.keras" to -> ./
Extract "dataset" to -> ./
  3. Install dependencies: 
    pip install -r requirements.txt
  4. Run the server: 
    python web.py
  5. Open index.html in your browser to start drawing and calculating.

Dev Notes:

Testing on Windows

# Create a new virtual environment
python -m venv venv

# Activate the virtual environment
.\venv\Scripts\activate || source venv/bin/activate

# Install Tensorflow
python3 -m pip install tensorflow[and-cuda]

# Keras to tflite
python convert_to_tflite.py -i model.keras -o model.tflite