You will need a way to detect dates, that's why I suggested ML. Since you are also using OAK devices, I thought you could leverage the computation power of dedicated hardware to speed up the AI interference. But ok, without ML.
To measure the dates, you need to know their positions on the image frame. A non ml option would be to have them on a highly contrasting background (eg. the conveyor belt is white). Then you would perform segmentation by basically grouping same colored pixels together to create blobs which would represent dates. Then you could apply an algorithm that would find the orientation and dimensions of each date.
In this case, your oak device would basically work as a normal camera since all processing would run on the host machine. You wouldn't be using depth either (or perhaps you could to aid in detection).