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Electronic Component Sorter
Electronic-Component-Sorter
E
Electronic Component Sorter
AI ModelDevelopment & Tools#AI#Machine Learning#Image Recognition#Electronic Components#Automation#OCRStandard PicksOpen Source
Overview :
Vanguard-s/Electronic-Component-Sorter is a project that automates the identification and classification of electronic components using machine learning and artificial intelligence. The project can categorize electronic components into seven major types: resistors, capacitors, LEDs, transistors, etc., using deep learning models, and further obtain detailed information about the components via OCR technology. Its significance lies in reducing manual categorization errors, increasing efficiency, ensuring safety, and assisting visually impaired individuals in identifying electronic components more conveniently.
Target Users :
The target audience includes electronic engineers, enthusiasts, and visually impaired individuals. Electronic engineers and enthusiasts can quickly and accurately identify and categorize electronic components using this product, significantly reducing human errors. Visually impaired individuals can utilize this technology to identify electronic components non-visually, enhancing their work efficiency and quality of life.
Total Visits： 474.6M
Top Region： US(19.34%)
Website Views ： 53.3K
Use Cases
Electronic engineers use this model to quickly categorize a large number of newly purchased electronic components.
Educational institutions utilize this model to teach students how to identify and categorize electronic components.
Visually impaired individuals use this technology to identify and use electronic components independently at home.
Features
Component Classification: Capable of identifying and categorizing seven types of electronic components including resistors, capacitors, LEDs, and transistors.
Detailed Information Query: One-click query for detailed information on integrated circuits, transistors, and capacitors.
User-Friendly Design: Simplistic and easy-to-use interface with clear titles, buttons, and text fields.
Real-time Image Capture and Processing: Capture images in real time through the camera and process them.
Model Integration: Integrate trained models into a Flask web application for a user-friendly interaction interface.
Challenging Problem Solving: Exploration and attempts to tackle challenging issues such as resistor color recognition and resistance calculation.
Scalability: The project plans to expand to more component categories and considers implementing SMD component recognition.
Mobile Application Development: Future development of a mobile app to leverage the high-quality advantages of smartphone cameras.
How to Use
1. Ensure Python 3.11.4 is installed and set up a virtual environment if necessary.
2. Download and extract the TRANSISTOR_OCR.rar file.
3. Modify the model and label paths in img_classf.py and capacitors.py.
4. Open the trans.py file and modify the recognition_model_path and detection_model_path.
5. Change the output directory path in main.py.
6. Install the libraries listed in requirements.txt and run python app.py to start the application.
7. Open the application interface and use the camera to capture images of electronic components.
8. The application will display recognition results, indicating the specific type of electronic component.
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