In particular, an embedded computer (with limited resources) analyzes live video stream from a traffic camera, trying to pick good frames that contain license plate numbers of passing cars. Once a plate is located, the frame is handed over to an OCR library to extract the registration and use it further.
In my country two types of license plates are in common use - rectangular (the typical) and square - actually, somewhat rectangular but "higher than wider", with the registration split over two rows.
(there are some more types, but let us disregard them; they are a small percent and usually belong to vehicles that lie outside our interest.)
Due to the limited resources and need for rapid, real-time processing, the maximum size of the network (number of cells and connections) the system can handle is fixed.
Would it be better to split this into two smaller networks, each recognizing one type of registration plates, or will the larger single network handle the two types better?