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The Project Handwritten Mathematical Problem solver uses CHROME dataset to train the model, where the system converts handwritten mathematical problem images to LaTex using TrOCR and solves that LaTex format problem using built in SymPy methods.

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sandipkatel/handwritten-mathematical-problem-solver

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Split the Terminal for Following

Client

This is a Next.js project bootstrapped with create-next-app.

Getting Started Client

  • First, install NodeJS following the link

  • Second, install node_modules package:

npm install
# or
yarn install
# or
pnpm install
# or
bun install
  • Third, run the development server:
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Open http://localhost:3000 with your browser to see the result.

Learn More

To learn more about Next.js, take a look at the following resources:

You can check out the Next.js GitHub repository - your feedback and contributions are welcome!

Deploy on Vercel

The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.

Check out our Next.js deployment documentation for more details.

Server

Getting Started on Backend

The server-side uses Flask API and requires following to be initialized

  • Create virtual environment python -m venv .venv
  • Run virtual environment .venv\Scripts\Activate
  • Exit from virtual environment deactivate
  • Install dependencies pip install -r requirements.txt
  • Navigate to backend/math_recognition_project
  • Run the server python manage.py runserver 8080
  • Later run python manage.py migrate

Note: If LF to URLF conversion warning occurs

Type the command below: unix2dos filename

About

The Project Handwritten Mathematical Problem solver uses CHROME dataset to train the model, where the system converts handwritten mathematical problem images to LaTex using TrOCR and solves that LaTex format problem using built in SymPy methods.

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