Hi !
I think you can use my code to detect and extract the grid, then run a "color detector" script on all the regions instead of the Tesseract recognizer, and return the RBG/HSL/... values in a text file.
I don't know if the region's pre-processing (denoizing, clear border, ...) will be useful though.
For the color detection, I found this tutorial : https://www.pyimagesearch.com/2014/08/04/opencv-python-color... which comes from Adrian Rosebrock (https://github.com/jrosebr1) who makes very great Python tutorials.
Hope it'll work !
You are right! I'm a student and my problem was some Supply Chain and Lean Management homeworks:
I needed to process computations on tables of numbers but my teacher didn't had the data, only screenshots of those tables, so I spent a long time copying numbers into Excel until I decided to implement this. :-)
Hi !
Tesseract is used to recognize numbers as strings, which are then casted into floats.
So it actually does recognize letters, but during the cast it will generate an error and outputs as numpy.inf ; but this was a choice of mine, and one can easily change the code to cast detections into integers, except when it is a letter in which case keep it as a string. :-)
The program runs with Python and Tesseract. It is quite fast (less than one second for a table of 100 numbers) though I never tested it with larger tables.
It detects numbers from an image of a table, which is supposed not to be rotated and also cropped : only the table is visible on the image. So, in order to process multiple tables per image, one needs to create an image for each table.
This program is rather simple I must say. ;-)
As for the handwriting, I think Tesseract can handle the recognition if the writing is good, but the table needs to fullfil the expected hypothesis. Also the pre-processing can't get rid of a lot of noise so it can be a problem too !
Hi !
Thank you for sharing this, it's a great tool I bumped into when searching for an image to CSV converter. But it seems to work with graphs only if I'm not mistaken.
