Abstract
The “Android Based Visual Product Identification for the Blind” mobile application helps visually impaired individuals identify products using their smartphone cameras. The application uses computer
vision and machine learning technologies to recognize various products, such as food items, household goods, or medications, and provides auditory feedback to the user. This system aims to empower visually impaired
individuals by enabling them to independently identify and interact with everyday objects, thereby enhancing their autonomy and quality of life.
Existing System
Visually impaired individuals often rely on tactile labeling methods, such as Braille or raised symbols, to identify products. Alternatively, they might require assistance from others to identify unfamiliar items. Some existing technologies, such as barcode scanners, can help with product identification, but they require precise alignment and might not be effective for all products. These limitations highlight the need for a more intuitive and accessible solution.
Proposed System
The proposed “Android Based Visual Product Identification for the Blind” system offers a more efficient and accessible solution. The application allows users to capture images of products using their smartphone camera. The system then analyzes the image using computer vision algorithms and a pre-trained machine learning model to identify the product. Once identified, the application provides auditory feedback, announcing the product name and any relevant information to the user.
Methodologies
- Agile Methodology: The project will follow Agile development practices, allowing for iterative progress, continuous user feedback, and quick adaptation to technical challenges.
- Prototyping: An initial prototype will be developed to test core functionalities such as image
recognition and auditory feedback, followed by enhancements based on user testing. - Model-View-Controller (MVC) Architecture: The system will be designed using MVC architecture.
Technologies Used
- Android SDK: For developing the mobile application interface and core functionality.
- Java/Kotlin: Programming languages used for Android development.
- TensorFlow Lite: For deploying machine learning models on mobile devices, allowing the app to perform real-time image recognition.
- OpenCV: A computer vision library used to process images captured by the camera, enhancing the accuracy of product identification.
- Text-to-Speech (TTS): For providing auditory feedback to users, converting recognized product names and information into speech.
- Firebase: For storing product data and user preferences in a cloud-based database, enabling real-time updates and data synchronization.
- RESTful APIs: For fetching product information from online databases or integrating with external services that provide detailed product descriptions.
System Features
- Real-Time Product Identification: Users can point their smartphone camera at a product,
and the app will identify it using computer vision and machine learning algorithms. - Auditory Feedback: The application provides spoken feedback, announcing the name of the
product and any additional information such as usage instructions or expiration dates. - Offline Functionality: The app can store a local database of frequently used products, allowing
for identification without an active internet connection. - Customizable User Preferences: Users can customize the type and amount of information provided
(e.g., only the product name, or additional details like nutritional information). - Product Database Integration: The app can be integrated with existing product databases to
provide comprehensive information on a wide range of items. - Barcode/QR Code Scanning: As an alternative identification method, the app can scan barcodes or
QR codes if available on the product. - Voice Commands: Users can interact with the app using voice commands, allowing for hands-free operation.
Benefits
- Increased Independence: The application enables visually impaired individuals to identify products on their own, reducing their reliance on others.
- Accessibility: By providing auditory feedback and supporting voice commands, the app is fully accessible to users with visual impairments.
- Convenience: The system simplifies the process of identifying products, making it quicker and more efficient than manual methods.
- Versatility: The app can be used in various settings, including grocery stores, pharmacies,
and at home, to identify a wide range of products. - Scalability: The system can be expanded to include a broader range of products.
