Hey everyone! I'm developing a tool to help customers select the right telehandler or lift based on their lifting needs – specifically how high, how far, and how heavy they need to lift. I have a lot of PDFs and PNGs from various manufacturers that contain load charts, which are typically represented as curved graphs indicating maximum weight capacity at different reach and height levels.
I'm looking for guidance on the best methods to convert this data into a JSON format that looks something like this:
{ "x": [ { "y": 1000 }, { "y": 800 } ], "x": [ { "y": 1500 }, { "y": 1000 } ] }
Here, 'x' represents the distance from the lift, 'y' is the height corresponding to 'x', and the numbers indicate the weight. Since some charts are vector graphics in PDFs while others are images (like PNGs), I want to know the most efficient way (manual, semi-automated, or fully automated) to extract this data. Any tips or tools would be super helpful! Thanks!
1 Answer
It sounds like you're building a really useful tool! If your goal is just to create a functioning search for lifts, manually entering data might actually be faster than developing an extensive program to extract it all. However, Python has great libraries for both image and PDF processing which might help extract that data semi-automatically. I'm curious about how your tool works, maybe I could use it one day!
That's right! I'm focusing on creating a solution that will streamline the selection process for my client’s customers. They'll be able to easily identify which lift suits their needs without spending too much time. Thanks for the insights!