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Systematic solution to translate early Brahmi Inscription context through computer vision and machine learning techniques.

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ManojLL/Brahmilator

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Brahmilator

Systematic approach to translate Early Brahmi Inscriptions

made-by-tyrants license total-lines

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This project aims to provide a systematic solution to recognize the characters in ancient Sri Lankan inscriptions and translate them into Sinhala and other native languages. Proposed system has 5 different core modules.

  1. Pre - Processing Module
  2. Character Segmentating Module
  3. Classification Module
  4. Words Generating Module
  5. Translating Module

Prerequisites

  • React-Native
  • Python 3.7
  • Tensorflow
  • MongoDB
  • OpenCV

How to run the project

  1. Clone the project by git clone https://github.com/ManojLL/Brahmilator.git
  2. Install all the mentioned dependecies by running npm install in root folder of the client-side component brahmi_frontend/tyrants
  3. Configuration between OpenCV and React-Native is mentioned on the brahmi_frontend README.md

How to contribute to this project

  • Make sure to read the contributing guidelines before contributing.
  • Fork this repository.
  • Clone your fork on local machine.
  • Switch to the master branch.
  • Make a new branch, preferably with the feature name.
  • Checkout to the new branch with git checkout <new_branch_name>
  • Do the desired changes and commit them with git commit -m '<a nice commit message here>'.
  • Push the change to your fork on GitHub with git push origin <your_branch_name>
  • Make a Pull Request to the master branch in the original repository, i.e. this one.

Note: Make sure you are always up to date with sync and pull upstream. Sync and pull before you start and before you push your work.

Resources

In order to get a clear understading of the project you can refer following links.

License

MIT

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Systematic solution to translate early Brahmi Inscription context through computer vision and machine learning techniques.

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