How to build an image processing model with new dataset using machine learning (Tensorflow)?

Question

I want to extract the data shown on an instrument panel cluster of a bike using image processing and ML (mostly Tensorflow). The sample of such cluster is

.

My input will be such complete image and I want to get the data such as speed shown by Speedometer, status of Neutral indicator(ON/OFF), Odometer reading etc from the cluster. Can anyone tell me what is the complete process for doing so? Or where are tutorials for the same? I am new to ML and there are lot of tutorials on the net but I dont know which one of them is useful for me.

Answer 1

There are a few things to consider here. You will first want to process the image a little before feeding it into a neural network for instance. Maybe crop out the section which contains the speedometer reading and perform some kind of OCR (Optical Character Recognition) on it for the speed reading.

The neutral indicator is a little easier actually, since there are only two states you can just look at a few sample images yourself and see which pixels actually change colors and write a quick check yourself, maybe something like so

(ON if pixel at (x,y) is green else OFF)  

Do the same for the odometer, just identify the area where the reading is, crop it out and perform OCR on the smaller image. So now really you're just looking for OCR tutorials using TensorFlow, if that's a constraint.

EDIT:

you can do all of this cropping and editing the image programatically in python using a library called cv2 which you can install with pip

 pip install opencv-python