I was thinking, with all of the AI improvements this year, perhaps there is a way to create a program a bit easier now, that can take old, barely readable schematics and redraw them. I know that the values of components often get lost to the haze but its usually party readable or completely readable but difficult. I thought It would be a nice idea to create a program or plugin that uses machine learning to identify the schematic symbols in a pdf or png or what have you, and redraw it using some schematic software. Obviously this would be a huge undertaking and far beyond my abilities but I asked ChatGPT-4 how one would go about it and I think it did a nice job outlining how to go about doing it. I would love it if someone would take the challenge. Perhaps someone is looking for a project idea. Here is ChatGPT-4's idea.
Data collection: Collect a dataset of old schematic diagrams and their corresponding clean and modern versions. This dataset will be used to train and validate the deep learning models.
Preprocessing: Preprocess the schematic diagrams by converting them to grayscale, resizing them to a standard size, and applying image enhancement techniques to improve the quality of the input images.
Line detection and segmentation: Use edge detection techniques such as the Canny edge detector or Hough transform to identify lines and other important features in the schematic diagrams. You may also need to segment the images to identify individual components and symbols.
Symbol recognition: Train a deep learning model, such as a convolutional neural network (CNN), to recognize and classify the symbols found in the schematic diagrams. You can use transfer learning to leverage pre-trained models and fine-tune them on your dataset.
Symbol placement and connection: Develop an algorithm to determine the correct placement and connections between symbols based on the detected lines and recognized symbols. This step may require the use of graph algorithms or rule-based systems.
Image generation: Once you have the clean and modern representation of the schematic, generate a new image or vector graphic using a suitable library or framework (e.g., OpenCV, PIL, or SVG).
Postprocessing: Apply any necessary postprocessing steps, such as smoothing or denoising, to improve the final output image.
Evaluation: Evaluate the performance of your program using appropriate metrics, such as accuracy, precision, recall, and F1-score. You may also want to obtain feedback from domain experts to assess the quality of the generated schematics.
Iteration: Continuously improve your models and algorithms by iterating through these steps, refining your approach based on evaluation results and feedback.
I wish I had the time or resources to do it but I do not. But what a great project idea.
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