Our team is developing a handwriting app for early diagnosis of dyslexia in children. We want to equip parents with the tools to identify and address this learning difficulty at its onset. Our team identified a need for early detection tools for dyslexia, particularly in regions like Indonesia where awareness is still growing.
We recognized that early diagnosis can significantly improve a child’s life trajectory, preventing dyslexia from impacting their adolescence and adulthood. Inspired by this, we decided to develop a handwriting application aimed at detecting dyslexia in children at the kindergarten, preschool, and elementary school levels.
Our goal is to equip parents with the necessary tools to identify and address this learning difficulty at its onset, fostering an environment where every child can thrive. This project is our step towards creating a more inclusive society.
| Student ID | Name | Learning Path |
|---|---|---|
| M008BSX1234 | Senopati Ajeng Sinta Ayu Nadya Rizki | Machine Learning |
| M120BSX1155 | Ambar Arum Prameswari | Machine Learning |
| M650BKY4491 | Fernando Sarimanella | Machine Learning |
| C405BSY3996 | Adrian Bimo Hernawan Pratama | Cloud Computing |
| C300BSY3577 | I Putu Dika Dharma Brasika | Cloud Computing |
| A120BSX2470 | Novi Ramadani | Mobile Development |
| A128BSY2334 | Johan Kevin Kenneth Hutagalung | Mobile Development |
This feature asks the user to scan handwriting to detect whether the person has Dyslexia. The system will output what percentage of users have Dyslexia and also a diagnosis.
The article feature contains various information which is expected to help and expand user awareness regarding Dyslexia diseases
This feature functions to display the user's scan history and diagnosis
Our group plans to add other disease detection to our application such as stunting and others by using input according to needs
Our group plans to make the dyslexia scanner app even more helpful to users by adding a chat feature for various diseases detected by our app over time.
- Find the suitable dataset.
- Develop and train a dyslexia detection model using TensorFlow.
- Implement TensorFlow Lite for model deployment on resource-constrained environments.
- Explore and integrate TensorFlow.js for model deployment.
- Design and implement a data pipeline for efficient model serving.
- Preprocess the dataset, including missing values imputation for enhanced model performance.
- Evaluate and fine-tune the model's hyperparameters for optimal performance.
- Developing an API to predict Dyslexia
- Creating a service using Firebase
- Create a Database to store prediction results
- Create a DockerFile and Build Docker Image
- Deploy using Cloud Run
- Creating UI design
- Configure android environment
- Developing the application using Kotlin
- Create database for article using realtime database from firebase
- API implementation from Cloud Computing in ScanActivity to post image and get results from the server