Running AI Models on multiple Embedded System

AI/ML/CV/OCR Project: Need to optimise this to run on a pi 2W. We have two models built separately one is for OCR in python(or you can configure some other good accuracy ocr), second is CNN Model: • MobileNetV2 96x96 0.35: The final layer consists of 16 neurons with a dropout rate of 0.1. • Another MobileNetV2 96x96 0.35 Variant: CNN Model: Accuracy - 72%, Loss - 0.56 • MobileNetV2 96x96 0.35: Accuracy - 74%, Weighted F1-score - 0.74 What we need you to do : We need to run these models on a embedded system (adruino/Raspberrypi etc check attachments for all the boards we need optimisation for) , The flow of the program is : One frame is captured via camera Frame is pre processed— we have this done, our model does a little pre processing but we need you to enhance it to make it better. OCR is run easyOCR , alphabets and numbers are grouped to represent values (our model does not locate ROI) around 5-6 regions of interest — need to add a validation step here as well. (You can select another model as well depends on you but it should be working for our use case) Then the second program runs a set of pre processing On the same image then runs by those models (you need to increase accuracy of the model). This is basically wave pattern recognition and classification. Image needs to be preprocessed for glares , distortion(like for wide angle or fish eye lens) , orientation, etc All this to be stored locally on embedded device and updated on cloud db as well(aws) , via chain encryption to ensure tamper proof logs. You need to optimise/merge both models together to get best efficiency and model should be optimized for embedded systems like embedded c or c+ etc. Need someone to handle this end to end.

Project ID: 39201053