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Explore Aksara Jawa effortlessly with YOLOv8 and Finite State Automata-powered Transliteration that i developed. Achieving high metrics (Train: 0.967/0.922/0.961, Validation: 0.966/0.924/0.961), our Streamlit interface ensures easy input and accurate output. Unlock precision and simplicity in Aksara Jawa to Latin conversion.

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AKSARA-TRANSLITERATION-USING-OBJECT-DETECTION-AND-AUTOMATA

Theofilus Arifin

license last-commit repo-top-language repo-language-count

Developed with the software and tools below.

Python JSON


Quick Links


Overview

Welcome to my Aksara Jawa Transliteration Website! Leveraging the cutting-edge technologies of YOLOv8 for accurate object detection and Finite State Automata for efficient transliteration, our platform ensures a seamless experience in converting Aksara Jawa script to Latin characters. Boasting impressive precision, recall, and mAP metrics of 0.967, 0.922, and 0.961 during training, and 0.966, 0.924, and 0.961 during validation, our model excels in providing reliable results. The user-friendly Streamlit interface allows users to effortlessly input Aksara Jawa text and receive precise Latin script equivalents. With a focus on advanced technology, robust metrics, and user-centric design, our platform stands out as a trustworthy solution for exploring the beauty of Aksara Jawa with confidence and accuracy.

Documentation Details


Repository Structure

└── Aksara-Transliteration-Using-Object-Detection-and-Automata/
    ├── codes
    │   ├── annotation.py
    │   ├── object_detection.py
    │   ├── preprocessing.py
    │   ├── projection_profile.py
    │   └── transliteration.py
    ├── images
    │   └── result
    │       └── result_label.txt
    ├── label
    │   └── labels.json
    ├── Main.py
    ├── model
    │   └── model.pt
    ├── pages
    │   └── 1_How_To_Use.py

Modules

.
File Summary
Main.py Error generating text for Main.py: Client error '429 Too Many Requests' for url 'https://api.openai.com/v1/chat/completions'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/429
codes
File Summary
annotation.py Error generating text for codes\annotation.py: Client error '429 Too Many Requests' for url 'https://api.openai.com/v1/chat/completions'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/429
object_detection.py Error generating text for codes\object_detection.py: Client error '429 Too Many Requests' for url 'https://api.openai.com/v1/chat/completions'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/429
preprocessing.py Error generating text for codes\preprocessing.py: Client error '429 Too Many Requests' for url 'https://api.openai.com/v1/chat/completions'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/429
projection_profile.py Error generating text for codes\projection_profile.py: Client error '429 Too Many Requests' for url 'https://api.openai.com/v1/chat/completions'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/429
transliteration.py Error generating text for codes\transliteration.py: Client error '429 Too Many Requests' for url 'https://api.openai.com/v1/chat/completions'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/429
images.result
File Summary
result_label.txt Error generating text for images\result\result_label.txt: Client error '429 Too Many Requests' for url 'https://api.openai.com/v1/chat/completions'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/429
label
File Summary
labels.json Error generating text for label\labels.json: Client error '429 Too Many Requests' for url 'https://api.openai.com/v1/chat/completions'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/429
pages
File Summary
1_How_To_Use.py Error generating text for pages\1_How_To_Use.py: Client error '429 Too Many Requests' for url 'https://api.openai.com/v1/chat/completions'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/429

Getting Started

Requirements

Ensure you have the following dependencies installed on your system:

  • Python: version 3.9.0

Installation

  1. Clone the Aksara-Transliteration-Using-Object-Detection-and-Automata repository:
git clone https://github.com/Theofilusarifin/Aksara-Transliteration-Using-Object-Detection-and-Automata
  1. Change to the project directory:
cd Aksara-Transliteration-Using-Object-Detection-and-Automata
  1. Install the dependencies:
pip install -r requirements.txt

Running Aksara-Transliteration-Using-Object-Detection-and-Automata

Use the following command to run Aksara-Transliteration-Using-Object-Detection-and-Automata:

python main.py

Contributing

Contributions are welcome! Here are several ways you can contribute:

  • Submit Pull Requests: Review open PRs, and submit your own PRs.
  • Join the Discussions: Share your insights, provide feedback, or ask questions.
  • Report Issues: Submit bugs found or log feature requests for Aksara-transliteration-using-object-detection-and-automata.
Contributing Guidelines
  1. Fork the Repository: Start by forking the project repository to your GitHub account.
  2. Clone Locally: Clone the forked repository to your local machine using a Git client.
    git clone https://github.com/Theofilusarifin/Aksara-Transliteration-Using-Object-Detection-and-Automata
  3. Create a New Branch: Always work on a new branch, giving it a descriptive name.
    git checkout -b new-feature-x
  4. Make Your Changes: Develop and test your changes locally.
  5. Commit Your Changes: Commit with a clear message describing your updates.
    git commit -m 'Implemented new feature x.'
  6. Push to GitHub: Push the changes to your forked repository.
    git push origin new-feature-x
  7. Submit a Pull Request: Create a PR against the original project repository. Clearly describe the changes and their motivations.

Once your PR is reviewed and approved, it will be merged into the main branch.


About

Explore Aksara Jawa effortlessly with YOLOv8 and Finite State Automata-powered Transliteration that i developed. Achieving high metrics (Train: 0.967/0.922/0.961, Validation: 0.966/0.924/0.961), our Streamlit interface ensures easy input and accurate output. Unlock precision and simplicity in Aksara Jawa to Latin conversion.

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