Microscope OCR

Microscope OCR is a powerful tool designed to recognize and identify electronic components through a microscope feed, providing automatic detection and essential information retrieval. This OCR functionality can be seamlessly integrated with any microscope setup that provides a digital image feed, making it versatile for both electronics hobbyists and professionals.

Project Overview

This project focuses on OCR-based recognition of small electronic components under a microscope. Once detected, the tool provides useful information like datasheets, color codes, and component specifications. Current functionality includes identifying resistors (via color codes), integrated circuits (ICs), and other common electronic components.

Key Features

• Universal Compatibility: Works with any microscope setup that outputs a digital feed.
• Graphical User Interface (GUI): User-friendly main interface (main.py) for capturing, processing, and analyzing microscope images.
• Component Detection:
  • Resistor Color Codes: Identifies resistance values from color bands.
  • IC Recognition: Detects IC part numbers and searches for datasheets.
  • Other Components: Expanding detection capabilities for diodes, capacitors, and more.
• Automated Datasheet Lookup: Retrieves datasheets and specifications for recognized components (integrated with datasheet-scraper.py).
• Image Preprocessing Tools: Includes scripts for cleaning OCR input (clear-ocr.py), inverting image colors (invert-color.py), and improved black (chip) detection.
• Histogram Analysis: Standalone histogram tool (histogram.py) for image statistics and pixel intensity analysis, with integration to capture frames from the main GUI.
• Image Export: Save and export processed images for documentation or further analysis.
• Customizable Area Selection: Select area for analysis using an input text field in the GUI.

Scripts Overview

• main.py: Main GUI for capturing and analyzing microscope images.
• histogram.py: Standalone histogram viewer and statistics tool.
• datasheet-scraper.py: Retrieves datasheets for detected components.
• clear-ocr.py: Preprocesses images to improve OCR results.
• invert-color.py: Inverts image colors for better contrast and detection.
• chip-detection-demo.py, ocr-text-demo.py, shape-detection-demo.py: Demos for specific detection and OCR tasks.

Getting Started

Prerequisites

To run the Microscope OCR tool, ensure you have the following software and libraries installed:

• Software Requirements:

  • Python 3.7 or higher
  • Tesseract OCR (ensure it is properly installed and configured)

• Python Libraries: Install the required libraries using pip:

  pip install opencv-python numpy BeautifulSoup4 requests

Running the Main GUI

To start the main graphical interface, run:

python main.py

Additional Tools

• To analyze an image histogram:

  python histogram.py

• For image preprocessing or OCR cleaning, use clear-ocr.py or invert-color.py as needed.

Future Enhancements

• Expanded Component Recognition: Develop recognition capabilities for additional components like transistors, capacitors, and inductors.
• User Interface: Further improve the GUI for easier interaction and functionality access.
• Improved OCR Accuracy: Implement additional preprocessing techniques to enhance OCR accuracy in various lighting conditions.

Contributing

Contributions are welcome! If you have suggestions for improvements or would like to add new features, please fork the repository and submit a pull request.

License

This project is licensed under the terms of the GNU General Public License v3.0. This means you can freely use, modify, and distribute the software, but you must keep the same license for any derivative works.

For a copy of the license, see the LICENSE file or visit GNU GPL v3.0.

Acknowledgments

• Tesseract OCR for the OCR engine.
• OpenCV for image processing capabilities.
• The open-source community for inspiration and collaboration.